Jobs Data Science

We are looking for a Middle AI/ML Engineer to develop and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will focus on building and iterating on machine learning and LLM-based solutions, integrating them into Foundry workflows to support analytics, automation, and decision-making. You will collaborate closely with data engineers, business analysts, and domain experts to deliver practical, production-ready AI solutions.

Responsibilities:

• Develop and enhance machine learning and AI models to support predictive analytics, classification, forecasting, and AI-assisted workflows.
• Build AI and ML solutions within Palantir Foundry, using Python and existing Foundry pipelines, Ontology objects, and workflows.
• Apply LLMs and NLP techniques (e.g. prompt engineering, fine-tuning, embeddings, retrieval-augmented workflows) using Palantir AIP for enterprise use cases.
• Collaborate with data engineers to understand data sources, ensure data quality, and prepare datasets for model training and inference.
• Conduct experiments, evaluate model performance, and iterate on features and model approaches.
• Integrate AI models into Foundry workflows to surface insights and support business processes.
• Support model deployment and monitoring by following established team standards and best practices.
• Work closely with business and domain stakeholders to translate requirements into practical AI-driven solutions.
• Document model behavior, assumptions, and limitations to support transparency and compliance.
• Stay up to date with applied AI and GenAI trends and contribute ideas under guidance from senior team members

Requirements:

• 3+ years of experience in machine learning, AI engineering, or applied data science.
• Strong Python skills; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Practical experience with RAG architectures, vector databases, and retrieval strategies
• Hands-on experience with LLMs, NLP, or GenAI use cases (e.g. prompt design, embeddings, text classification, summarization).
• Practical understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and iteration.
• Experience working with structured data (tabular, time series); exposure to text or unstructured data is a plus.
• Familiarity with enterprise data environments and collaborative development workflows.
• Ability to clearly explain model results and AI behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to have: 

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

Rx360 is building a wearable and AI companion that helps seniors live independently and stay connected to the people who care about them. Our mission is to give older adults confidence in their everyday lives, and to give their families peace of mind.
Our team works across hardware, firmware, software and AI. We move quickly, care deeply about the people we build for, and hold a high bar for quality and trust.
We're looking for a Data Scientist to turn what our wearable senses into intelligence that keeps seniors safe. You'll build the machine learning behind fall detection and fall-risk prediction, and the health and activity insights our AI companion shares with seniors and their supporters. You'll own models from raw sensor data and labels through validation and deployment, on the band and in the cloud.

What You Will Do
• Build and own ML models for fall detection on the Rx360 band, using IMU, barometer and PPG signals, with high sensitivity and very few false alarms
• Develop fall-risk and fall-prevention models that spot changes in gait, activity, sleep and heart rate over days and weeks, so supporters can act before a fall happens
• Build our datasets from real-world wear by older adults, including data collection, labeling and ground-truth processes with pilot and clinical partners
• Design validation plans and success metrics, such as sensitivity, false alarms per wearer-day and time to alert, and run validation studies in pilots
• Work with firmware engineers to run models on the band within tight memory, compute and battery limits, through feature design, model compression and quantization
• Define the sensor data we capture and how it flows from band to cloud, and build the pipelines and tooling that keep it clean, labeled and usable for training
• Turn sensor data into everyday insights, such as steps, activity, sleep, heart rate trends, wear time and anomalies, for our AI companion and supporter app
• Monitor model performance in the field, investigate misses and false alarms, and retrain and improve models over time
• Partner with product, hardware, firmware, software, clinical and AI teams to shape requirements and decide what to build next
• Handle health data responsibly, with privacy, consent and security built into how we collect, store and use it
• Communicate results, trade-offs and risks clearly to technical and non-technical colleagues

What You'll Bring
• 5+ years of experience in data science or machine learning, including models shipped to production

• Required: at least 2 hours of overlap each day with U.S. Central time (CT) working hours to collaborate with our U.S. team
• Hands-on experience with time-series sensor data, such as accelerometer, gyroscope or PPG, including signal processing and feature engineering
• Strong Python and experience with deep learning frameworks such as PyTorch or TensorFlow, along with classical ML methods
• Experience with rare-event problems and imbalanced data, and sound judgment on evaluation, error analysis and the cost of false alarms versus misses
• Experience deploying models to resource-constrained or embedded devices, or working closely with firmware engineers to do so
• Experience designing data collection and labeling, and building data pipelines (SQL, cloud data tools)
• Excellent written and spoken English
• Comfort working remotely and asynchronously across time zones
• Curiosity, persistence and a bias for action
• Empathy for older adults and the people who care for them

Nice to Have
• Background in digital health, wearables or connected medical devices, including clinical validation studies
• Experience with on-device ML tools such as TensorFlow Lite for Microcontrollers or Edge Impulse
• Familiarity with health data privacy and regulatory requirements (GDPR, HIPAA, FDA or MDR)
• Startup experience through early launches
How to Apply
Send your CV, a short note about a model you took from raw sensor or real-world data to production and what you learned along the way, and a sample of your work (a repo, notebook, paper or write-up, redacted if needed).
Rx360 is an equal opportunity employer. We welcome candidates of all backgrounds and do not discriminate on the basis of any protected characteristic.

About the Role

We are seeking an experienced Python and Airtable specialist to help us consolidate, cleanse, and normalise customer data from multiple sources into our existing Airtable platform. Our primary objective is to rank customers by their commercial value, enabling us to identify and prioritise our most valuable customers effectively.
 

You will work with data snapshots from MongoDB and SQL databases, integrate them into our pre-configured Airtable setup, and establish an automated sync process for ongoing updates. We need someone who can independently assess our existing setup and implement a reliable, low-maintenance solution. 20h/week commitment.

 

Key Responsibilities
✅ Assess existing Airtable configuration and understand the current data structure

✅ Extract and consolidate data from MongoDB and SQL database snapshots

✅ Cleanse and normalise data to ensure consistency and quality across sources

✅ Understand and implement customer ranking methodology based on commercial value

✅ Import processed data into our existing Airtable platform via API

✅ Design and implement automated data sync - either live feed or monthly automated updates

✅ Recommend best practices for data integration and ongoing maintenance

✅ Document the solution for future reference and minimal manual intervention

Requirements
✔ Strong Python programming skills with focus on data processing

✔ Proven experience with Airtable API and data integration

✔ Expertise in MongoDB - querying, data extraction, and manipulation

✔ Solid SQL knowledge - complex queries and data extraction

✔ Data cleansing and normalisation experience across multiple data sources

✔ ETL (Extract, Transform, Load) process implementation

✔ Automated data syncing and scheduling experience

✔ Ability to work independently and assess existing systems without extensive briefing

✔ Problem-solving skills to figure out requirements from existing setup and data snapshots

✔ Strong attention to detail and data quality standards

Nice to Have
➕ Experience with customer value analysis and ranking algorithms

➕ Knowledge of data warehousing concepts and best practices

➕ Familiarity with Apache Airflow or similar workflow automation tools

➕ Experience with API rate limiting and optimization

➕ Background in CRM data integration projects

➕ Understanding of data governance and security best practices

➕ Experience with Pandas, NumPy or similar Python data libraries

➕ Knowledge of cloud-based automation platforms (AWS Lambda, Google Cloud Functions, etc.)

Note: We will provide you with direct access to our Airtable account and links to updated data snapshots. You'll need to independently assess the setup and determine the best integration approach without extensive briefing from our previous consultant.

Join Sigma Software to help build advanced machine learning solutions for one of the large-scale players in the programmatic advertising ecosystem. 

We are looking for a Senior Machine Learning Engineer with strong production ML expertise and deep interest in real-time optimization systems, large-scale behavioral data, and AdTech challenges.

In this role, you will work with a dedicated Sigma Software team on a predictive modeling platform integrated with a live ad exchange processing hundreds of millions of auction requests daily. You will contribute to sophisticated ML solutions involving bid optimization, calibration, counterfactual evaluation, and constrained decision-making systems.

We as a company offer the opportunity to work on technically challenging products, collaborate with experienced engineers and data scientists, and make a direct impact on large-scale production systems.

CUSTOMER

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a high-scale ad exchange platform and is investing in predictive decisioning capabilities to improve advertising performance, audience targeting, and campaign optimization through advanced machine learning technologies.

PROJECT

The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform evaluates and filters advertising supply in real time, predicts high-performing audience contexts, builds look-alike audiences from small seed datasets, and optimizes campaign performance across multiple business objectives and operational constraints.

The team works on complex machine learning challenges including censored bid-landscape modeling, sparse and delayed conversion attribution, calibration systems, counterfactual evaluation, and constrained optimization models. The solution is designed for large-scale production use and close collaboration with the Customer’s internal data science organization.

RESPONSIBILITIES

• Build and improve censored bid-landscape models to estimate clearing-price distributions from partially observed auction data
•  Develop real-time win probability estimation models responsive to bid pricing dynamics
• Design and implement hierarchical lift estimation models with confidence-bound-based selection strategies
• Build conversion propensity models using sparse, delayed, and aggregate-only labels
• Develop look-alike audience modeling approaches using positive-unlabeled learning and embedding-based nearest-neighbor techniques
• Implement advertiser-level calibration strategies while independently monitoring ranking and calibration quality
• Design robust offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting
• Define exploration strategies and propensity logging approaches to ensure reliable downstream correction and evaluation
• Develop constrained optimization mechanisms for campaign objectives, pricing constraints, and volume targeting
• Contribute to data diagnostics, capability assessments, and evidence-based model recommendations
• Collaborate with the Customer team during post-launch tuning and performance validation cycles
• Prepare technical documentation and knowledge transfer materials for the Customer’s internal data science team
• Participate in architecture discussions and contribute to scalable ML platform design decisions

REQUIREMENTS

• 5+ years of experience in Machine Learning or Data Science with production-grade models measured against business KPIs
• Strong Python skills including numpy, pandas, and scikit-learn
• Strong SQL skills and experience working with large-scale datasets
• Deep practical experience with XGBoost, LightGBM, or CatBoost
• Strong understanding of regularization, calibration methods, and categorical feature handling
• Strong knowledge of probability, statistics, confidence intervals, and statistical power analysis
• Experience with feature engineering for structured and behavioral datasets
• Hands-on experience with Spark or PySpark
• Practical knowledge of experimentation frameworks and A/B testing methodologies
• Experience with advanced validation approaches including temporal splits, leakage detection, drift analysis, and slice-based metrics
• Understanding of explainability techniques such as SHAP and permutation importance
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanics
• Experience working with sparse, delayed, or censored labels
• Knowledge of attribution modeling, survival analysis, and positive-unlabeled learning
• Practical experience with counterfactual and off-policy evaluation techniques
• Understanding of calibration methods including isotonic regression and Platt scaling
• Experience with hierarchical, empirical-Bayes, or partial-pooling models
• Knowledge of constrained or multi-objective optimization approaches
• Experience with uplift modeling and causal inference methods
• Experience with Vertex AI or similar managed ML training environments
• Publications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech

PERSONAL PROFILE

• Strong analytical and problem-solving skills
• Ability to work effectively in a highly data-driven environment
• Strong communication and stakeholder management abilities
• Ability to explain complex modeling decisions to technical and non-technical audiences
• Proactive mindset with strong ownership mentality
• Attention to detail and scientific rigor in experimentation and evaluation

Join Sigma Software to help build advanced machine learning solutions for one of the large-scale players in the programmatic advertising ecosystem. We are looking for a Senior Machine Learning Engineer with strong production ML expertise and deep interest in real-time optimization systems, large-scale behavioral data, and AdTech challenges.

In this role, you will work with a dedicated Sigma Software team on a predictive modeling platform integrated with a live ad exchange processing hundreds of millions of auction requests daily. You will contribute to sophisticated ML solutions involving bid optimization, calibration, counterfactual evaluation, and constrained decision-making systems.

We as a company offer the opportunity to work on technically challenging products, collaborate with experienced engineers and data scientists, and make a direct impact on large-scale production systems.

CUSTOMER

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a high-scale ad exchange platform and is investing in predictive decisioning capabilities to improve advertising performance, audience targeting, and campaign optimization through advanced machine learning technologies.

PROJECT

The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform evaluates and filters advertising supply in real time, predicts high-performing audience contexts, builds look-alike audiences from small seed datasets, and optimizes campaign performance across multiple business objectives and operational constraints.

The team works on complex machine learning challenges including censored bid-landscape modeling, sparse and delayed conversion attribution, calibration systems, counterfactual evaluation, and constrained optimization models. The solution is designed for large-scale production use and close collaboration with the Customer’s internal data science organization.

Job Description

• Build and improve censored bid-landscape models to estimate clearing-price distributions from partially observed auction data
•  Develop real-time win probability estimation models responsive to bid pricing dynamics
• Design and implement hierarchical lift estimation models with confidence-bound-based selection strategies
• Build conversion propensity models using sparse, delayed, and aggregate-only labels
• Develop look-alike audience modeling approaches using positive-unlabeled learning and embedding-based nearest-neighbor techniques
• Implement advertiser-level calibration strategies while independently monitoring ranking and calibration quality
• Design robust offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting
• Define exploration strategies and propensity logging approaches to ensure reliable downstream correction and evaluation
• Develop constrained optimization mechanisms for campaign objectives, pricing constraints, and volume targeting
• Contribute to data diagnostics, capability assessments, and evidence-based model recommendations
• Collaborate with the Customer team during post-launch tuning and performance validation cycles
• Prepare technical documentation and knowledge transfer materials for the Customer’s internal data science team
• Participate in architecture discussions and contribute to scalable ML platform design decisions

Qualifications

• 5+ years of experience in Machine Learning or Data Science with production-grade models measured against business KPIs
• Strong Python skills including numpy, pandas, and scikit-learn
• Strong SQL skills and experience working with large-scale datasets
• Deep practical experience with XGBoost, LightGBM, or CatBoost
• Strong understanding of regularization, calibration methods, and categorical feature handling
• Strong knowledge of probability, statistics, confidence intervals, and statistical power analysis
• Experience with feature engineering for structured and behavioral datasets
• Hands-on experience with Spark or PySpark
• Practical knowledge of experimentation frameworks and A/B testing methodologies
• Experience with advanced validation approaches including temporal splits, leakage detection, drift analysis, and slice-based metrics
• Understanding of explainability techniques such as SHAP and permutation importance
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanics
• Experience working with sparse, delayed, or censored labels
• Knowledge of attribution modeling, survival analysis, and positive-unlabeled learning
• Practical experience with counterfactual and off-policy evaluation techniques
• Understanding of calibration methods including isotonic regression and Platt scaling
• Experience with hierarchical, empirical-Bayes, or partial-pooling models
• Knowledge of constrained or multi-objective optimization approaches
• Experience with uplift modeling and causal inference methods
• Experience with Vertex AI or similar managed ML training environments
• Publications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech

PERSONAL PROFILE

• Strong analytical and problem-solving skills
• Ability to work effectively in a highly data-driven environment
• Strong communication and stakeholder management abilities
• Ability to explain complex modeling decisions to technical and non-technical audiences
• Proactive mindset with strong ownership mentality
• Attention to detail and scientific rigor in experimentation and evaluation

Our Customer:

Our customer develops a digital employee experience platform that monitors endpoint and virtual desktop performance for enterprise organizations. The platform handles millions of events per minute across an international customer base.

Your Tasks:

• Build production-grade Java services and high-throughput data-processing components
• Develop, optimize and maintain large-scale Kafka and Apache Flink streaming pipelines
• Integrate data from multiple products into a unified organizational data model
• Implement employee, department, device and RBAC-related functionality
• Build services and APIs using Java, TypeScript, gRPC and Protobuf
• Design and optimize persistence on PostgreSQL, ClickHouse and Redis, including schemas, indexes and data-access patterns
• Improve scalability, reliability, throughput, latency, observability and operational cost
• Diagnose production bottlenecks across distributed pipelines, services and databases
• Develop TypeScript and React frontend capabilities when needed
• Collaborate with architecture, product and DevOps teams
• Use AI-assisted and agentic development tools while maintaining correctness, security and code quality

Required Experience and Skills:

• 6+ years of experience building and maintaining production distributed, high-throughput systems
• 5+ years of professional Java experience, including concurrency, memory management and JVM performance tuning
• 3+ years of hands-on production experience with Apache Kafka, including partitioning, consumer groups, schemas, delivery guarantees, fault tolerance and throughput tuning
• 2+ years of hands-on production experience with Apache Flink, including stateful processing, windowing, checkpointing, state management, backpressure and fault recovery
• Proven track record of personally building, scaling and operating systems processing millions of events per minute
• Deep understanding of time-series and large-scale analytical databases, including modeling, ingestion, query tuning and partitioning
• Hands-on experience with PostgreSQL or another relational database
• Strong command of Protobuf, including schema evolution and compatibility
• Working knowledge of gRPC and service-to-service communication
• Hands-on experience deploying, monitoring and troubleshooting distributed workloads on Kubernetes
• Hands-on experience with Docker and CI/CD pipelines
• Solid understanding of production observability and incident resolution
• Hands-on use of AI-assisted and agentic coding tools in production, including critical review of generated code
• Proven ability to deliver independently, make architectural tradeoffs and identify risks early
• Strong communication skills for explaining technical decisions and working with international teams
• English – Upper-Intermediate

Would Be a Plus:

• Hands-on experience with ClickHouse
• Hands-on experience with Redis
• Hands-on experience with TypeScript and React

Join our team to shape the technical direction of a data platform that makes complex industrial data usable, reliable, and scalable. Build reusable frameworks, internal tools, and automation that simplify customer onboarding and help engineering teams deliver safer, more consistent data pipelines. Work closely with engineers and domain experts to strengthen data quality, platform reliability, and developer experience while applying AI pragmatically to improve engineering productivity.

Our Client was established with the mission to fundamentally transform the execution of capital projects and operations. Designed by industry experts for industry experts, our Client’s platform empowers users to digitally search, visualize, navigate, and collaborate on assets. Drawing on 30 years of software expertise and 180 years of industrial legacy as part of the renowned Scandinavian business group, Client plays an active role in advancing the global energy transition. The company operates from Norway, the UK, and the U.S.

Key Responsibilities:

• Shape the technical direction of the data platform, tooling, and engineering standards
• Design and improve reusable frameworks, internal tools, and automation for data pipeline development
• Define standards for testing, CI/CD, quality gates, and platform reliability
• Develop reusable approaches to data modeling, schemas, data contracts, validation, and schema evolution
• Improve observability, data quality, lineage, and developer experience across the platform
• Turn recurring delivery challenges into scalable platform capabilities that reduce customer onboarding effort
• Apply AI to engineering workflows, establishing clear guardrails and measuring its impact on productivity and quality
• Lead technical design discussions, mentor engineers, and guide architectural decisions and technical trade-offs
• Document best practices and support the adoption of standard platform patterns across teams
• Evaluate architectural decisions against time-to-value, cost-to-serve, and delivery predictability

Requirements:

• Data Platform Engineering: Extensive experience designing, building, and evolving scalable data platforms and production data pipelines
• Technical Leadership: Experience leading technical design, mentoring engineers, and establishing engineering standards across teams
• Programming: Strong software engineering skills, with experience developing reusable frameworks, tooling, and automation for data processing
• Data Architecture: Strong understanding of data modeling, schema evolution, data contracts, and validation across multiple source systems
• Testing and CI/CD: Practical experience implementing automated testing, integration testing, quality gates, and deployment pipelines
• Platform Reliability: Experience improving observability, diagnosing operational issues, and building reliable data services
• Data Quality and Lineage: Experience implementing approaches that detect data issues early and make data flows easier to understand and troubleshoot
• Developer Experience: Ability to simplify engineering workflows and turn repeated manual work into reusable, maintainable tools
• AI-Assisted Engineering: Ability to identify practical AI use cases, evaluate their effectiveness, and define safeguards for quality and reliability
• Architectural Judgment: Ability to balance technical quality, delivery speed, operational cost, and long-term maintainability
• English Proficiency: C1+ with the ability to communicate technical concepts and architectural decisions clearly
• Interpersonal Skills: Strong collaboration skills and the ability to build alignment across engineering teams and domain experts

Хелоу, люба спільното \0/

Ми продовжуємо комплектувати західні команди нашими спеціалістами, і цього разу в нас супер цікавий клієнт та персонажі.

Це квант-команда з п'яти людей всередині одного з найбільших європейських multi-strategy хедж-фондів, під управлінням понад $1 млрд. Керує нею розумник, який закінчив університет у 14 та отримав PhD у Гарварді в 17 оО

Їм потрібні 1-2 Quantitative Developers/Analysts, які дуже добре пишуть код і не роблять помилок. Це front-office: ваші моделі та код працюють з реальними грошима, і «потестити на проді» тут не вийде.
 
Потрібне одне з двох: досвід із ринками, трейдингом чи квант-дослідженнями, або сильна математична база, яку можна показати (освіта, наукові роботи, олімпіади). Решті команда навчить, тому дивимось і на талановитих джунів/мідлів, і на досвідчених.
 

Особливості пропозиції:

• Команда розумників.
• Strong Verbal English (C1+) на ділі.
• Full-time, remote, графік гнучкий.
• Старт протягом 1-2 тижнів.
   

ІМХО, роль цікава якщо:

• Хочеш front-office у фонді на мільярд, не переїжджаючи в Лондон чи Емірати
• Хочеш будувати кар'єру в команді розумників
• Шариш в темі
   

Наймаємо в 3 етапи:
1. Знайомство зі мною. Обговоримо роль та ваше бачення себе в ній.
2. Розмова з командою фонду.
3. PROFIT

На цьому я дозволю собі перемкнутись на англійську, бо далі конкретніше.
 

What you'll do

• Do quantitative research across markets, portfolios and investment strategies
• Build, test and maintain models for forecasting, valuation, portfolio construction, risk and signal generation
• Acquire, clean and reconcile large datasets, with controls for data quality and reproducibility
• Run back-tests, stress tests and model validation; apply time series, optimisation, econometrics and ML where suitable
• Write reliable research code, tools and automated workflows, and take research into production
• Turn findings into concise insights for portfolio managers
   

What you need

• Strong programming in one of: Python, R, MATLAB, C++
• One of the two: experience with financial markets, trading, risk or quant research, or a strong quantitative background you can show (a degree in mathematics, statistics, physics, computer science, econometrics or finance; research; olympiads)
• Statistical modelling and model evaluation on real datasets
• Precision: you test your own work and catch your own mistakes
• Really good English, spoken and written
   

Will be a plus

• Working knowledge of financial instruments, portfolio concepts and market risk
• SQL and Git
• MSc / PhD, CFA or FRM
• Public code, research papers
   

Soft skills

• Hungry and driven: you know what a front-office seat is worth
• You say "I am not sure" before it costs money
   

The Team

• Head of the team (UAE), you report to him
• His No. 2 (London)
• Quant Team Lead
• 2x Researchers (Europe, California)

For fulltime players we offer:

• $2000-6000 depending on level, with a clear path up.
• 15 paid day-offs and 10 European holidays.
• Flexible schedule, fully remote from Ukraine or Europe.

About Keymakr
We take care of all aspects of Data Set Preparation, Annotation, and Labeling for video and images.
We adhere to a personalized approach, providing a tailored solution for each of our clients.
Our brilliant in-house R&D team personalizes the annotation tools for each client based on their needs, so we can successfully tackle even the most complicated requirements the client might have.
Use cases: Self-driving cars, Facial Recognition & Security, Medical Annotations, Drones, Robotics, and any industry you can think of.

We are seeking a Solution Engineer to develop technical solutions for data annotation projects, implement AI tools, and automate workflows.

In this role, you will work with Computer Vision and NLP/LLM technologies, helping to improve the efficiency, quality, and profitability of projects.

Key Responsibilities

1. Technical support for POCs: setting up tools and workflows, validating data and results, identifying and resolving technical issues during pilot project launches.
2. Research and implementation of AI tools: finding and testing data annotation platforms, setting up pre-annotation and model-assisted labeling, working with SAM and YOLO models, and evaluating their effectiveness.
3. Development of AI solutions and automation: creating scripts, bots, and small applications using Claude, ChatGPT, and similar AI tools; automating data processing, validation, and conversion; integrating services via API; supporting the Rocket.Chat bot. Technical project support: solving non-standard problems in 2D/3D Computer Vision, video annotation, and NLP/LLM, configuring platforms, and assisting project teams.
4. Testing and optimization: verifying solution functionality, finding errors, preparing bug reports and requirements for Keylabs improvements, and finding ways to reduce labeling time and costs.
5. Documentation and training: preparing technical instructions, developing a knowledge base, and training colleagues on new tools and processes.

What we expect

1. Understanding of data labeling processes for Computer Vision (2D/3D) and NLP/LLM.
2. Analytical thinking, initiative, and the ability to independently learn new technologies and tools.
3. Practical experience solving problems using Claude, ChatGPT, or similar AI services.
   Basic knowledge of Python and API principles.
4. Understanding of JSON, XML, CSV, COCO, and YOLO data formats.
5. Understanding of pre-annotation and model-assisted labeling principles.
6. Software testing skills, error detection, and result validation.
7. Experience with data annotation platforms such as Keylabs, CVAT, Labelbox, SuperAnnotate, or similar solutions.
8. Ability to structure information, create technical documentation, and clearly describe technical issues.

Important: Commercial development experience and the ability to write code from scratch are not required. Logical thinking, confidence in using AI tools, the ability to independently understand technical problems, and bring solutions to a working state are key.

Experience with fine-tuning models is an advantage.

1. Proficiency in Docker.
2. Experience with LiDAR and point clouds.
3. Prompt engineering and LLM response validation skills.
4. English proficiency sufficient for reading technical documentation and communicating with international clients.

Working Conditions

1. Salary: UAH 30,000 gross.
2. Fully remote work.
3. Flexible schedule.
4. Opportunity to work with modern AI tools and participate in the development of technical solutions for real-world projects.
5. Opportunity for professional development in AI, Computer Vision, NLP/LLM, and process automation.

We look forward to welcoming you to our team!

Dataforest are looking for a highly experienced Team Lead Data Science with a strong engineering and managerial background (10+ years in the domain). In this role, you will lead and scale our AI/Data Science practice, establish robust development processes, drive pre-sales and R&D activities, and effectively manage an engineering team of 8–10+ members.
 

 What you’ll do:

• Team Leadership & Management: Lead and grow the Data Science department (8–10+ people), perform 1-on-1s, technical assessments, and performance reviews.
• Architecture & Strategy: Define the AI/DS architecture and tech strategy, establish high-quality coding standards, and oversee code reviews.
• Business & Pre-sales: Collaborate closely with C-level executives (CEO/CTO) to shape development processes, lead pre-sale activities, and estimate complex AI projects for international clients.
• R&D & Solution Delivery: Drive complex AI projects from concept to production (High-Load systems, LLM/RAG solutions) and conduct R&D with cutting-edge technologies.
   

 You'll be a great fit if you have:

• 10+ years of total experience in Data Science, Machine Learning, Data Engineering, and Software Engineering.
• Management Experience: Proven track record of leading teams of 8+ engineers (Data Scientists, ML Engineers).
• AI & LLM Expertise: Deep practical knowledge of the modern AI stack: LLM, RAG, LangChain, LangGraph, and AI Agents.
• Core Tech Stack:
  • Mastery of Python, algorithms, and data structures.
  • Proven experience in building scalable ML/DS architectures and high-load production systems.
  • Solid understanding of MLOps, Docker, Kubernetes, and CI/CD pipelines.
• Business Analysis & Pre-sales: Demonstrated capability in project estimation, client communication, and translating complex business requirements into technical solutions.
• English: Upper-Intermediate (B2+) or higher for fluent communication with international clients.

We ensure your growth with:

• Great networking opportunities with international clients and challenging tasks;

• Building interesting projects from scratch using new technologies;

• Personal and professional development opportunities;

• Competitive salary fixed in USD;

• Paid vacation and sick leave;

• Flexible work schedule;

• Friendly working environment with minimal hierarchy;

• Team-building activities and corporate events.

United Tech is a global IT product company shaping the future of real-time social connection.

With millions of users across North America, Europe, LATAM, and MENA, we build next-gen mobile and web apps for live-streaming and social networking.

Our platforms enable connection at scale fast, interactive, and deeply engaging.

The market is projected to exceed $206B by 2030, and we are already leading the evolution.

Founded in Ukraine, scaling worldwide. Are you in?

About the role: This is a role for a leader who thrives in a fast-moving product environment, where challenges fuel growth and every decision shapes the future. With United Tech you will have the freedom to design, build, and re-engineer processes from the ground up, working side by side with a team that values initiative, knowledge sharing, and bold goals. Your ideas will directly influence revenue, and you will see the tangible results of your work reflected in key financial metrics. We move fast, we cut through bureaucracy, and we take on complex challenges that push both personal and professional boundaries, limited only by the scale of your ambitions

In this role, you will

• Build, scale, and develop the Machine Learning team, including hiring, mentoring, and performance evaluation
• Define and execute a Machine Learning strategy aligned with business priorities, and plan the department’s roadmap
• Identify and drive new ML initiatives
• Analyze existing algorithms and define the best ways to improve them
• Prioritize initiatives based on business impact (ROI, time-to-market)
• Oversee the full lifecycle of ML projects, from problem formulation to deploying models into production, ensuring execution and quality of results
• Stay hands-on: contribute directly to the implementation of key ML projects
• Provide regular reporting to stakeholders on a weekly, monthly, and quarterly basis
• Collaborate closely with product managers, analysts, engineers, and C-level executives

It’s all about you

• Proven ability to implement best practices in Data Science: reproducibility, A/B testing, ML monitoring
• Strong track record in maintaining model quality (performance, drift, latency)
• Advanced Python skills (pandas, sklearn, numpy, xgboost; pytorch/tf is a plus)
• High-level SQL expertise (large datasets, query optimization)
  Hands-on experience with ML pipelines, orchestration (Airflow, Prefect), and monitoring (Evidently, MLflow, Prometheus)
• Experience with GCP
• Solid understanding of A/B testing, causal inference, and statistics
• Familiarity with architecture fundamentals (API, data pipelines, microservices — integration level)
• Cloud experience with AWS or Azure
• Knowledge of distributed computation tools (e.g., Spark, CloudRun)
• Proven track record with generative AI/NLP model deployment
• Experience in startups or high-growth companies
• Publications, conference speaking, achievements on Kaggle, or open-source contributions

Would be a plus

• Experience optimizing company processes through AI solutions
• Implementation of AI tools across departments (development, support, marketing, etc.)

What we offer

Care and support:

• 20 paid vacation days, 15 sick leave days, and 6 additional days off for family events
• 100% medical insurance coverage
• Sports and equipment reimbursement
• Team building events, corporate gifts, and stylish merch
• Financial and legal support
• Maternity Recovery Support Allowance
• Position retention and support for those who join the Armed Forces of Ukraine
• Participation in social initiatives supporting Ukraine

Comfortable working environment:

• Work from our Kyiv hub or remotely with a flexible schedule
• Modern equipment or depreciation of your own tools

Investment in your future:

• Collaborate with a highly-skilled team of Middle & Senior professionals, sharing practical cases and expertise in the social networking niche
• 70% of our heads and leads have grown into their roles here – so can you!
• Performance-oriented reviews and Individual Development Plans (IDPs)
• Reimbursement for professional courses and English classes
• Corporate library, book club, and knowledge-sharing events
• AI is an integral part of our work culture. We invest in modern AI tools, equip our teams with the skills to use them effectively, and foster an environment where technology helps people achieve better results faster
   

Hiring process

• Intro call
• Technical Interview
• ML System Design Interview
• Final Interview
• Reference check
• Offer

A product software company that has been developing computational systems for machine learning and big data analysis is looking for a Data Scientist for a remote role.

REQUIREMENTS:

- 2+ years of experience in DS/ML;

- Python experience (2 years);

- Hands-on experience with Time Series for at least 6 months;

- Practical skills with NumPy, Jupyter Notebook, PyTorch;

- Intermediate level of English or higher (ability to communicate with the company's clients).

COMPANY OFFERS:

- Employment as a private entrepreneur;

- Working hours from 10:00 to 19:00 (Kyiv time);

- Fully remote work;

- Career and professional development;

- Financial bonuses.

As a member of Computer Vision Team, you will contribute to data preparation, research, prototyping, and the deployment of machine learning models and computer vision algorithms in edge and cloud environments. Your responsibilities will also include monitoring and evaluating solution performance in production and adapting the system to rapidly changing operational conditions.
Required Qualifications:

• 3+ years of experience in computer vision
• High proficiency in Python
• Strong experience with deep learning and the PyTorch framework for computer vision tasks
• Research experience in computer vision with a focus on delivering results
• Ability to work independently or within a small team, with a strong sense of ownership over results

Nice to Have:

• Experience with deep learning solutions for detection, tracking, image retrieval, and segmentation tasks
• Understanding of geometric computer vision principles
• Model optimization: quantization, pruning, neural network compilers
• Hands-on experience implementing low-level CV algorithms
• Knowledge of C++

The Defense Procurement Agency (DOT) is a national agency responsible for procuring ammunition, UAVs, military equipment, fuel, food, clothing, and other critical supplies for the Armed Forces of Ukraine and the Defense Forces.

Our mission: Strengthening Ukraine by ensuring reliable support for the Defense Forces while fostering the development of the national defense industry.

Our vision: To become the driving force behind the transformation of Ukraine's defense procurement ecosystem—not only operating the system, but continuously improving it.

We are currently looking for a Master Data Specialist who will be responsible for coordinating and maintaining master data management processes, ensuring data quality, accuracy and consistency, and collaborating with internal stakeholders to improve related business processes and information systems.

Responsibilities

• Organize, coordinate and oversee master data management processes, including creating new master data records, updating existing ones, and ensuring their accuracy, completeness and consistency.
• Prevent duplicate master data records and ensure compliance with established policies, methodologies and standards.
• Handle requests from internal departments regarding the creation and modification of master data records, and provide guidance on master data management and the use of relevant information system functionality.
• Manage master data classification and labeling processes, considering their impact on related business processes, data routing, item allocation, reporting and further use across the company's information systems.
• Support the migration of master data from the legacy CRM system to the new one.
• Gather, analyze and document business requirements from internal stakeholders to prepare technical specifications for information system development and improvements.
• Coordinate collaboration between business departments and the IT team during the implementation of approved changes and functional enhancements. Evaluate implementation results and ensure that the delivered functionality meets business requirements.

Requirements

• Bachelor's or Master's degree in Information Technology, Computer Science, Business Analytics, Management, Economics or a related field.
• Confident user of Microsoft Excel, with strong data processing and analysis skills.
• Understanding of master data and reference data management principles, including data quality, accuracy and consistency.
• Experience working with ERP/CRM systems and other enterprise information systems.
• Understanding of business processes and the impact of master data on related systems and reporting.
• Strong attention to detail, a structured and systematic approach, and the ability to work with large volumes of data.
• Ability to communicate and collaborate effectively with internal stakeholders and IT teams.

Will be a plus

• Basic knowledge of SQL.
• Experience in business requirements analysis and documentation, including preparing technical specifications for information system enhancements.
• Experience in testing and verifying that implemented changes meet business requirements.

What We Offer

• Meaningful work that directly contributes to strengthening Ukraine's defense capabilities.
• Official employment with competitive salary and full social benefits.
• Professional development, learning opportunities, and career growth.
• Structured onboarding and dedicated mentorship.
• A collaborative, supportive, and mission-driven team.
• Modern office near the metro with reliable internet and equipped shelter.
• Opportunity to build and lead cybersecurity for one of Ukraine's most strategically important digital ecosystems.

By joining our team, you will contribute to one of Ukraine's largest digital transformation initiatives in the defense sector and help build the future of defense procurement.

Ready to make a real impact? We'd love to hear from you. Apply today!

* By submitting your CV, you consent to the processing of your personal data in accordance with applicable legislation.

** Due to the high volume of applications, only candidates whose qualifications best match the position will be contacted.

As a Generative AI Data Scientist, you will play a critical role in use case evaluation, architecture, and development planning, developing and implementing state-of-the-art generative models to solve complex business problems. This is an exciting opportunity to apply cutting-edge techniques and push the boundaries of AI technology.

Qualifications:

-Proven experience in developing and implementing generative models, such as GANs, VAEs, or deep generative models.

- Strong proficiency in programming languages such as Python, with experience using deep learning frameworks such as TensorFlow or PyTorch.

- Solid understanding of machine learning, deep learning, and statistical modeling concepts.

- Experience working with large-scale datasets and preprocessing techniques.

- Proficiency in data visualization and exploratory analysis tools.

- Strong problem-solving skills and ability to think creatively to design innovative solutions.

- Excellent written and verbal communication skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.

- Proven ability to work collaboratively in a team environment and contribute to cross-functional projects.

- Strong research and self-learning abilities, with a passion for staying up-to-date with the latest advancements in generative AI and related fields.

Preferred Skills:

- Experience with natural language processing (NLP) and text generation models.

- Familiarity with cloud-based machine learning platforms and Generative AI services, such as Azure (Open AI, ChatGPT), Google Cloud or AWS.

- Knowledge of parallel computing and distributed training frameworks.

- Publications or contributions to the research community in the field of generative AI or related disciplines.

Responsibilities:

- Research and Development: Conduct research and stay up-to-date with the latest advancements in generative AI, deep learning, and related fields. Explore and experiment with different generative models, architectures, and algorithms to enhance our capabilities.

- Model Development: Design, develop, and implement novel generative models tailored to specific use cases. Create and optimize deep neural networks, variational autoencoders (VAEs), generative adversarial networks (GANs), or other generative architectures to generate realistic and diverse synthetic data.

- Data Preprocessing: Work closely with data engineers and domain experts to preprocess and clean large-scale datasets. Apply statistical techniques and data augmentation methods to ensure high-quality input data for training generative models.

- Model Training and Evaluation: Train and fine-tune generative models using large-scale datasets, leveraging techniques such as transfer learning and unsupervised learning. Develop evaluation metrics and benchmarks to assess model performance and generate insights to guide model improvements.

- Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, insurance experts, and experience designers, to integrate generative models into real-world applications. Provide technical guidance and support to ensure successful implementation and deployment of generative AI solutions.

- Innovation and Optimization: Continuously explore new techniques, frameworks, and tools to optimize and enhance the performance of generative models. Stay informed about emerging trends and best practices in the field and contribute to the advancement of the organization's data science capabilities.

- Documentation and Reporting: Prepare clear and concise technical documentation, including model architectures, methodologies, and experiment results. Present findings and insights to both technical and non-technical stakeholders, contributing to knowledge-sharing and decision-making processes.