Jobs Kyiv

MacPaw is a software company that develops and distributes software for macOS and iOS. Today, we have 20 million active users across all our products.

At MacPaw, we believe humans and technology can reach their greatest potential together.

MacPaw is proud to be Ukrainian. The support and development of Ukraine are significant parts of the company’s culture. MacPaw gathers open-minded people who support each other and aspire to change the world around us.

We are looking for an AI Datasets Lead (data generation & annotation) to join our AI team to oversee the entire data annotation process, supporting our AI and machine learning initiatives. 

This role requires strong leadership skills combined with a good understanding of data collection and annotation for machine learning models.
In this role, you will be central to ensuring the efficiency, accuracy, and success of our data labeling processes.

If it sounds interesting to you, then look no further — send us your CV!

In this role, you will:

• Organize and supervise the data annotation process, ensuring its accuracy and quality
• Build and lead the data annotation team, coordinate team tasks and workflows, and foster the team development
• Implement quality control processes, analyze errors, and improve standards
• Develop and maintain annotation guidelines and documentation to ensure consistency and accuracy across the team
• Optimize annotation processes and explore automation opportunities
• Closely collaborate with data engineers to develop efficient data processing solutions and tools, and implement automated data processing workflows to streamline annotation processes
• Collaborate closely with ML Engineers to align data annotation efforts with machine learning model requirements and understand the neural network training process
• Take part in cross-functional coordination and cooperation with other colleagues
• Manage costs related to data sourcing and annotation
• Explore outsourcing & alternative data solutions, manage cooperation with external freelancers and partners, including searching, selecting, and organizing cooperation with them to meet the organizational needs
• Ensure compliance with data privacy and security regulations throughout the data annotation process

Skills you’ll need to bring:

• Proven experience leading a data annotation team
• Basic understanding of AI/ML concepts (data annotation needs, task types, neural network training)
• Good knowledge of data labeling processes and familiarity with relevant tools, platforms, and best practices in the data annotation field
• Basic understanding of data collection processes (web scraping, dataset management)
• Strong leadership, communication, and organizational skills with the ability to convey technical aspects to both technical and non-technical stakeholders
• Strong ability to manage uncertainty and bring clarity to unstructured situations — handle incoming requests without predefined processes, structure the chaos, and progressively build clear, actionable workflows for the team
• Experience with quality assurance processes for large datasets
• Keen attention to detail and the ability to balance multiple projects and priorities simultaneously
• Openness to frequent feedback from multiple stakeholders and readiness to quickly adapt processes in real time based on that input
• Experience with data privacy regulations and ethical considerations in data annotation
• Strong analytical skills to monitor processes and implement improvements based on data-driven insights
• Ability to develop and implement data quality metrics and KPIs to measure the effectiveness of annotation processes
• Experience managing budgets & external partnerships
• Intermediate level of English or higher

What we offer:

• We are a Ukrainian company, and we stand with Ukraine against the russian aggression
  We maintain workplaces for the mobilized Macpawians and provide financial support to colleagues or their families affected by the war. Here, you can also read about the MacPaw Foundation, which intends to help save the lives of Ukrainian defenders and provide relief to as many civilians as possible: https://macpaw.foundation/.
• We are committed to our veterans
  Our Veteran Career and Empowerment Program is designed to ensure our veterans and active military personnel receive the recognition, support, and opportunities they deserve.
• Hybrid work model
  Whether to work remotely or at the hub is entirely up to you. If you decide to mix it, our Kyiv office, which works as a coworking space, is open around the clock. The office is supplied with UPS and Starlink for an uninterrupted work process.
• Your health always comes first
  We guarantee medical insurance starting on your first working month. For those abroad, you can receive a yearly Medical insurance allowance as compensation for managing your medical expenses.
• Flexible working hours
  You can choose a schedule that is comfortable for you. No one here tracks your clock in/out because MacPaw is built on trust and cooperation.
• Space to grow both professionally and personally
  Education opportunities to grow both hard and soft skills, annual development reviews, and internal community.
• Teams we are proud of
  We build honest, transparent, and reliable relationships within teams. Every Macpawian can improve processes and implement their ideas. We encourage open and constructive feedback and provide training for Macpawians on giving and receiving feedback.
• Office designed for people (and pets)
  Our office has it all: a spacious workplace with enough room for sitting up, lying down, and running around; a gym for recreation; cozy kitchens; a sleeping/meditation room; and a terrace with a view where we throw summer parties. Also, we have two cats living in the office.
• Time-off policy that covers life’s needs
  Convenient personal time-off policy to help you take care of essential matters in your personal life, and parental leaves. On top of all that, sabbaticals are open after 5 years of being with MacPaw.
• Join social initiatives with MacPawCares
  MacPaw participates in numerous humanitarian aid and charity projects across many fields, and you are welcome to jump in to make the world a better place.
• We’re an equal-opportunity employer. Here is a safe place for applicants of all backgrounds
  We are hiring talented humans. Meaning with all our variety of backgrounds and identities, including service members and veterans, women, members of the LGBTQIA+ community, individuals with disabilities, and other often underrepresented groups. MacPaw does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
  
  *Some benefits are under development, and new adjustments are possible.

Overview

We are seeking a highly skilled and experienced Senior/Lead Computer Vision Engineer specializing in Navigation to join our innovative R&D team. In this pivotal role, you will drive the development and deployment of state-of-the-art computer vision algorithms for autonomous navigation systems, contributing to our efforts in robotics, autonomous vehicles, drones, or similar fields. You will work cross-functionally with engineering, product, and research teams to deliver robust, real-time solutions that enable safe and intelligent navigation in dynamic environments.

Responsibilities

• Lead the design, development, and optimization of computer vision algorithms for localization, mapping, and navigation.
• Develop and implement algorithms for object detection, segmentation, SLAM, 3D scene reconstruction, visual odometry, and sensor fusion (using cameras, LiDAR, IMUs, etc.).
• Guide the integration of computer vision modules with navigation and control systems, ensuring seamless operation in real-world conditions.
• Collaborate with software, hardware, and product teams to define requirements and deliver scalable, robust navigation solutions.
• Stay current with advancements in deep learning, computer vision, and robotics, and introduce relevant state-of-the-art techniques into the product.
• Design and execute experiments to evaluate performance and robustness; analyze results and iterate on solutions.
• Prepare technical documentation, progress reports, and presentations for internal and external stakeholders.

Requirements

• 5+ years of experience in computer vision, preferably in navigation, robotics, or autonomous systems.
• Master’s or PhD in Computer Science, Robotics, Electrical Engineering, or related field.
• Strong proficiency in Python and/or C++.
• Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and classical computer vision libraries (e.g., OpenCV, PCL).
• Experience in deploying and optimizing models for single-board computers such as Raspberry Pi, Nvidia Jetson
• Proven track record of developing and deploying real-time vision algorithms for navigation tasks in challenging environments.
• Extensive knowledge of SLAM, visual odometry, sensor fusion, and related algorithms.
• Experience with ROS, embedded systems, and real-time software development is a plus.
• Excellent problem-solving skills, strong analytical mindset, and effective communication abilities.

Preferred Qualifications

• Knowledge of SLAM and related models.
• Familiarity with the MAVLink protocol and ArduPilot.
• Familiarity with edge computing or real-time GPU-based inference.
• Publications or contributions to the open-source community in vision or robotics.

EPAM GenAI Consultants are changemakers who bridge strategy and technology—applying agentic intelligence, RAG, and multimodal AI to transform how enterprises operate, serve users, and make decisions. 

Preferred Tech stack 

 Programming Languages 

• Python (*) 
• TypeScript 
• Rust 
• Mojo 
• Go 

 Fine-Tuning & Optimization 

• LoRA (Low-Rank Adaptation) 
• PEFT (Parameter-Efficient Fine-Tuning) 

Foundation & Open Models 

• OpenAI (GPT series), Anthropic Claude Family, Google Gemini, Grok (*, at least one of them ) 
• Llama 
• Falcon 
• Mistral 

Inference Engines  

• VLLM 

 Prompting & Reasoning Paradigms (*) 

• CoT (Chain of Thought) 
• ToT (Tree of Thought) 
• ReAct (Reasoning + Acting) 
• DSPy 

Multimodal AI Models 

• CLIP (*) 
• BLIP2 
• Whisper 
• LLaVA 
• SAM (Segment Anything Model) 

 Retrieval-Augmented Generation (RAG) 

• RAG (core concept) (*) 
• RAGAS (RAG evaluation and scoring) (*) 
• Haystack (RAG orchestration & experimentation) 
• LangChain Evaluation (LCEL Eval) 

Agentic Frameworks 

• CrewAI  (*) 
• AutoGen, AutoGPT, LangGraph, Semantic Kernel, LangChain (* at least  2 of them) 
• Prompt Tools:  PromptLayer, PromptFlow (Azure),  Guidance by Microsoft (* at least one of them) 

Evaluation & Observability 

• RAGAS – Quality metrics for RAG (faithfulness, context precision, etc.) (*) 
• TruLens – LLM eval with attribution and trace inspection (*) 
• EvalGAI – GenAI evaluation testbench 
• Giskard – Bias and robustness testing for NLP 
• Helicone – Real-time tracing and logging for LLM apps 
• HumanEval – Code generation correctness testing 
• OpenRAI – Evaluation agent orchestration 
• PromptBench – Prompt engineering comparison 
• Phoenix by Arize AI – Multimodal and LLM observability 
• Zeno – Human-in-the-loop LLM evaluation platform 
• LangSmith – LangChain observability and evaluation 
• WhyLabs – Data drift and model behavior monitoring 

Explainability & Interpretability (understanding) 

• SHAP 
• LIME 

Orchestration & Experimentation (*) 

• MLflow 
• Airflow 
• Weights & Biases (W&B) 
• LangSmith 

 Infrastructure & Deployment 

• Kubernetes 
• Amazon SageMaker 
• Microsoft Azure AI 
• Goggle Vertex AI  
• Docker 
• Ray Serve (for distributed model serving) 

Responsibilities 

• Lead GenAI discovery workshops with clients
• Design Retrieval-Augmented Generation (RAG) systems and agentic workflows
• Deliver PoCs and MVPs using LangChain, LangGraph, CrewAI , Semantic Kernel,  DSPy, RAGAS 
• Ensure Responsible AI principles in deployments (bias, fairness, explainability) 
• Support RFPs, technical demos, and GenAI architecture narratives 
• Reuse of accelerators/templates for faster delivery 
• Governance & compliance setup for enterprise-scale AI 
• Use of evaluation frameworks to close feedback loops 

Requirements 

• Consulting: Experience in exploring the business problem and converting it to applied AI technical solutions; expertise in pre-sales, solution definition activities 
• Data Science: 3+ years of hands-on experience with core Data Science, as well as knowledge of one of the advanced Data Science and AI domains (Computer Vision, NLP, Advanced Analytics etc.)   
• Engineering: Experience delivering applied AI from concept to production, familiarity, and experience with MLOps, Data, design of Data Analytics platforms, data engineering, and technical leadership 
• Leadership: Track record of delivering complex AI-empowered and/or AI-empowering programs to clients in a leadership position. Experience in managing and growing a team to scale up Data Science, AI, and ML capabilities is a big plus. 
• Excellent communication skills (active listening, writing and presentation), drive for problem solving and creative solutions, high EQ 
• Experience with LLMOps or GenAIOps tooling (e.g., guardrails, tracing, prompt tuning workflows) 
• Understanding of the importance of AI products evaluation is a must 
• Knowledge of cloud GenAI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI) 
• Understanding of data privacy, compliance, and Governance in GenAI (GDPR, HIPAA, SOC2, RAI, etc.) 
• In-depth understanding of a specific industry or a broad range of industries. 

We are looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms.

The ideal candidate will have experience with SLAM, Visual-Inertial Odometry (VIO), and sensor fusion.

We consider engineers at Middle/Senior levels — tasks and responsibilities will be adjusted accordingly.

Required Qualifications:

• 3+ years of hands-on experience with classical computer vision
• Knowledge of popular computer vision networks and components 
• Understanding of geometrical computer vision principles
• Hands-on experience in implementing low-level CV algorithms
• Practical experience with SLAM and/or Visual-Inertial Odometry (VIO)
• Proficiency in C++
• Experience with Linux
• Ability to quickly navigate through recent research and trends in computer vision.

Nice to Have:

• Experience with Python
• Familiarity with neural networks and common CV frameworks/libraries (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion.

We are looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms. 

The ideal candidate will have experience with Object tracking, Visual-Inertial Odometry (VIO) and sensor fusion. 

We consider engineers at Middle+ and Senior levels - tasks and responsibilities will be adjusted accordingly. 

Required Qualifications: 

• 2+ years of hands-on experience with classical computer vision
• Understanding of geometrical computer vision principles 
• Hands-on experience in implementing low-level CV algorithms
• Proficiency in C++ 
• Experience with Linux 
• Experience with Object tracking and detection tasks 
• Ability to quickly navigate through recent research and trends in computer vision. 
• Familiarity with neural networks and common CV frameworks/libraries (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.) 

Nice to Have: 

• Practical experience with SLAM and/or Visual-Inertial Odometry (VIO) 
• Experience with Python 
• Experience with sensor fusion. 

We’re looking for a Senior Data Scientist to help shape how our clients build and scale AI solutions on AWS. In this role, you’ll develop and deploy cutting-edge generative AI models on SageMaker – from model training and fine-tuning to optimized deployment – guiding customers from ideation to production through proof of concept. You’ll work closely with startup founders, technical leaders, and account teams to create scalable, high-impact AI solutions that drive real business value.

Responsibilities:

• Model Development & Deployment: Deploy and train models on AWS SageMaker (using TensorFlow/PyTorch).
• Model Tuning & Optimization: Fine-tune and optimize models using techniques like quantization and distillation, and tools like Pruna.ai and Replicate.
• Generative AI Solutions: Design and implement advanced GenAI solutions, including prompt engineering and retrieval-augmented generation (RAG) strategies.
• LLM Workflows: Develop agentic LLM workflows that incorporate tool usage, memory, and reasoning for complex problem-solving.
• Scalability & Performance: Maximize model performance on AWS’s by leveraging techniques such as model compilation, distillation, and quantization and using AWS specific features.
• Collaboration: Work closely with Data Engineering, DevOps, and MLOps teams to integrate models into production pipelines and workflows.

Requirements:

• 4+ years of experience in machine learning or data science roles, with deep learning (NLP, LLMs) expertise.
• Expert in Python and deep learning frameworks (PyTorch/TensorFlow), and hands-on with AWS ML services (especially SageMaker and Bedrock).
• Proven experience with generative AI and fine-tuning large language models.
• Strong experience deploying ML solutions on AWS cloud infrastructure and familiarity with MLOps best practices.
• Excellent communication skills and ability to work directly with customers in a consulting capacity.
• A master’s degree in a relevant field and AWS ML certifications are a plus.

Benefits:

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program – enjoy cooperation with your colleagues and get a bonus 
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes

• Soft skills training

   

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all and believes that a diverse workforce is crucial to our success. Our recruitment decisions are based on your experience and skills, recognizing the value you bring to our team.

We are seeking an experienced Senior/Middle Data Scientist with a passion for large language models (LLMs) and cutting-edge AI research. In this role, you will focus on designing and prototyping data preparation pipelines, collaborating closely with data engineers to transform your prototypes into scalable production pipelines and actively develop model training pipelines with other talented data scientists. Your work will directly shape the quality and capabilities of our models by ensuring we feed them the highest-quality, most relevant data possible. The datasets you build directly determine model capability, safety, and cost, raising downstream task accuracy, reducing training waste, and accelerating time-to-market for product teams.

What you will do

• Design, prototype, and validate data preparation and transformation steps for LLM training datasets, including cleaning and normalization of text, filtering of toxic content, de-duplication, de-noising, detection and deletion of personal data, etc.
• Formation of specific SFT/RLHF datasets from existing data, including data augmentation/labeling with LLM as teacher.
• Analyze large-scale raw text, code, and multimodal data sources for quality, coverage, and relevance.
• Develop heuristics, filtering rules, and cleaning techniques to maximize training data effectiveness.
• Collaborate with data engineers to hand over prototypes for automation and scaling.
• Research and develop best practices and novel techniques in LLM training pipelines.
• Monitor and evaluate data quality impact on model performance through experiments and benchmarks.
• Research and implement best practices in large-scale dataset creation for AI/ML models.
• Document methodologies and share insights with internal teams.

Qualifications and experience needed

Education & Experience:

• 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP.
• Proven experience in data preprocessing, cleaning, and feature engineering for large-scale datasets of unstructured data (text, code, documents, etc.).
• An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

NLP Expertise:

• Good knowledge of natural language processing techniques and algorithms.
• Hands-on experience with modern NLP approaches, including embedding models, semantic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs.
• Familiarity with LLM training and fine-tuning techniques, and data requirements.

ML & Programming Skills:

• Proficiency in Python and common data science and NLP libraries (pandas, NumPy, scikit-learn, spaCy, NLTK, langdetect, fasttext).
• Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.
• Ability to write efficient, clean code and debug complex model issues.

Data & Analytics:

• Solid understanding of data analytics and statistics.
• Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.
• Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.

Deployment & Tools:

• Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.
• Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).
• Experience with cloud platforms (AWS, GCP, or Azure) and big data technologies (Spark, Hadoop, Ray, Dask) for scaling data processing or model training is a plus.

Communication & Personality:

• Experience working in a collaborative, cross-functional environment.
• Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies.
• Ability to rapidly prototype and iterate on ideas

A plus would be

Advanced NLP/ML Techniques:

• Familiarity with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.
• Understanding of FineWeb2 or a similar processing pipeline approach

Research & Community:

• Publications in NLP/ML conferences or contributions to open-source NLP projects.
• Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations)

Domain & Language Knowledge:

• Familiarity with the Ukrainian language and context.
• Understanding of cultural and linguistic nuances that could inform model training and evaluation in a Ukrainian context.
• Knowledge of Ukrainian text sources and data sets, or experience with multilingual data processing, can be an advantage given our project’s focus.

MLOps & Infrastructure:

• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).
• Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models.

Problem-Solving:

• Innovative mindset with the ability to approach open-ended AI problems creatively.
• Comfort in a fast-paced R&D environment where you can adapt to new challenges, propose solutions, and drive them to implementation.

What we offer

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace.
• Remote onboarding.
• Performance bonuses.
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners.
• Health and life insurance.
• Wellbeing program and corporate psychologist.
• Reimbursement of expenses for Kyivstar mobile communication.

Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and «all-things-digital» solutions, is looking for an Incident Editorial Specialist to join our dynamic team.

Join our night shift editorial team in Lviv and Kyiv and work on real-time traffic incident editing for a global mapping platform.

No prior experience required — we provide full training. It’s a great way to start your career in IT and gain experience on international projects.

This is a full-time, remote role. You’ll work with English-language data, applying clear rules and maintaining focus during night shifts.

Requirements:

• B2+ level English proficiency
• High attention to detail
• Confident working with multiple tabs/windows, fast typing

• Basic familiarity with Google Maps, traffic/navigation apps

   

Benefits:

• 8.7-hour fixed night shift, including:
  • 8 paid working hours
  • 40min unpaid break
• Shifts scheduled between 19:00 and 07:00 (exact shift hours depend on assignment)
• 5 shifts per week, including possible weekend rotations
• Paid vacation and official holidays in line with company policy

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
 

About the client:
Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.
 

About the role:
We are looking for an experienced Senior Data Scientist / NLP Lead to spearhead the development of cutting-edge natural language processing solutions for the Ukrainian LLM project. You will lead the NLP team in designing, implementing, and deploying large-scale language models and NLP algorithms that power the products.

This role is critical to the mission of advancing AI in the Ukrainian language context, and offers the opportunity to drive technical decisions, mentor a team of data scientists, and shape the future of AI capabilities in Ukraine.
 

Requirements:
Education & Experience:
- 5+ years of experience in data science or machine learning, with a strong focus on NLP.
- Proven track record of developing and deploying NLP or ML models at scale in production environments.
- An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

NLP Expertise:
- Deep understanding of natural language processing techniques and algorithms.
- Hands-on experience with modern NLP approaches, including embedding models, text classification, sequence tagging (NER), and transformers/LLMs.
- Deep understanding of transformer architectures and knowledge of LLM training and fine-tuning techniques, hands-on experience developing solutions on LLM, and knowledge of linguistic nuances in Ukrainian or other languages.

Advanced NLP/ML Techniques:
- Experience with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.
- Background in information retrieval or RAG (Retrieval-Augmented Generation) is a plus for building systems that augment LLMs with external knowledge.
ML & Programming Skills:
- Proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn).
- Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.
- Ability to write efficient, clean code and debug complex model issues.

Data & Analytics:
- Solid understanding of data analytics and statistics.
- Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.
- Experience on how to build a representative benchmarking framework given business requirements for LLM.
- Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.

Deployment & Tools:
- Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.
- Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).
- Experience with cloud platforms (AWS, GCP or Azure) and big data technologies (Spark, Hadoop) for scaling data processing or model training is a plus.
- Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).
Leadership & Communication:
- Demonstrated ability to lead technical projects and mentor junior team members.
- Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies clearly.

Responsibilities:
- Lead end-to-end development of NLP and LLM models - from data exploration and model prototyping to validation and production deployment. This includes designing novel model architectures or fine-tuning state-of-the-art transformer models (e.g., BERT, GPT) to solve project-specific language tasks.
- Analyze large text datasets (Ukrainian and multilingual corpora) to extract insights and build robust training datasets.
- Guide data collection and annotation efforts to ensure high-quality data for model training.
- Develop and implement NLP algorithms for a range of tasks such as text classification, named entity recognition, semantic search, and conversational AI.
- Stay up-to-date with the latest research to apply transformer-based models, embeddings, and other modern NLP techniques in the solutions.
- Establish evaluation metrics and validation frameworks for model performance, including accuracy, factuality, and bias.
- Design A/B tests and statistical experiments to compare model variants and validate improvements.
- Deploy and integrate NLP models into production systems in collaboration with engineers - ensuring models are scalable, efficient, and well-monitored in a real-world setting.
- Optimize model inference and troubleshoot issues such as model drift or data pipeline bottlenecks.
- Provide technical leadership and mentorship to the NLP/ML team.
- Review code and research, uphold best practices in ML (version control, reproducibility, documentation), and foster a culture of continuous learning and innovation.
- Collaborate cross-functionally with product managers, software engineers, and MLOps engineers to align NLP solutions with product goals and infrastructure capabilities.
- Communicate complex data science concepts to stakeholders and incorporate their feedback into model development.

The company offers:
- Competitive salary.
- Equity options in a fast-growing AI company.
- Remote-friendly work culture.
- Opportunity to shape a product at the intersection of AI and human productivity.
- Work with a passionate, senior team building cutting-edge tech for real-world business use.

Join Burny Games — a Ukrainian company that creates mobile puzzle games. Our mission is to create top-notch innovative games to challenge players’ minds daily.

What makes us proud?

• In just two years, we’ve launched two successful mobile games worldwide: Playdoku and Colorwood Sort. We have paused some projects to focus on making our games better and helping our team improve.
• Our games have been enjoyed by over 45 million players worldwide, and we keep attracting more players.
• We’ve created a culture where we make decisions based on data, which helps us grow every month.
• We believe in keeping things simple, focusing on creativity, and always searching for new and effective solutions.

What are you working on?

• Genres: Puzzle, Casual
• Platforms: Mobile, iOS, Android, Social

Team size and structure?

130+ employees

Key Responsibilities:

• Build and maintain ML for product and marketing teams
• Develop predictive systems for personalization, recommendations, and dynamic game content
• Automate data workflows and create reliable, scalable ML pipelines from feature engineering to deployment
• Monitor model performance, detect drift, and ensure ongoing accuracy and stability of ML systems
• Partner with Product, Marketing, and Engineering to integrate ML solutions into live games and operational workflows
• Own DS/ML projects end-to-end: from defining the problem to production deployment and iteration
• Share knowledge, conduct code reviews, and promote best practices across the data team

About You:

• 4+ years of experience in Data Science or ML, with a track record of delivering production models (2+ years in gamedev or consumer apps businesses)
• Strong background in statistical modeling, forecasting, and machine learning
• Advanced programming skills in Python or R (pandas, numpy, scikit-learn, PyTorch/TensorFlow or tidyverse, caret, mlr), writing clean and maintainable code
• Excellent SQL skills, confident with large-scale datasets and cloud data warehouses (BigQuery, Snowflake, Redshift)
• Experience deploying, monitoring, and maintaining ML models in production environments
• Strong problem-solving mindset, able to translate business and product goals into ML solutions
• Clear communicator who can explain complex models and systems to both technical and non-technical teams
• Passion for gaming and curiosity about player behavior

Will Be a Plus:

• Experience building user-level LTV forecasting models
• Background in recommender systems, personalization, or contextual bandits
• Familiarity with MLOps practices and tools
• Experience with ETL/orchestration frameworks (dbt, Dataform, Airflow)
• We run on GCP — experience with BigQuery, Vertex AI, Pub/Sub, and Cloud Run/Functions

What we offer:

• 100% payment of vacations and sick leave [20 days vacation, 22 days sick leave], medical insurance.
• A team of the best professionals in the games industry.
• Flexible schedule [start of work from 8 to 11, 8 hours/day].
• L&D center with courses.
• Self-learning library, access to paid courses.
• Stable payments.

The recruitment process:

CV review → Interview with TA manager → Interview with Head of Analytics → Final Enterview → Job offer

If you share our goals and values and are eager to join a team of dedicated professionals, we invite you to take the next step.

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
 

About the client:
Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.
 

About the role:
We are looking for an experienced Senior Data Scientist / NLP Lead to spearhead the development of cutting-edge natural language processing solutions for the Ukrainian LLM project. You will lead the NLP team in designing, implementing, and deploying large-scale language models and NLP algorithms that power the products.

This role is critical to the mission of advancing AI in the Ukrainian language context, and offers the opportunity to drive technical decisions, mentor a team of data scientists, and shape the future of AI capabilities in Ukraine.
 

Requirements:
Education & Experience:
- 5+ years of experience in data science or machine learning, with a strong focus on NLP.
- Proven track record of developing and deploying NLP or ML models at scale in production environments.
- An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

NLP Expertise:
- Deep understanding of natural language processing techniques and algorithms.
- Hands-on experience with modern NLP approaches, including embedding models, text classification, sequence tagging (NER), and transformers/LLMs.
- Deep understanding of transformer architectures and knowledge of LLM training and fine-tuning techniques, hands-on experience developing solutions on LLM, and knowledge of linguistic nuances in Ukrainian or other languages.

Advanced NLP/ML Techniques:
- Experience with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.
- Background in information retrieval or RAG (Retrieval-Augmented Generation) is a plus for building systems that augment LLMs with external knowledge.
ML & Programming Skills:
- Proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn).
- Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.
- Ability to write efficient, clean code and debug complex model issues.

Data & Analytics:
- Solid understanding of data analytics and statistics.
- Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.
- Experience on how to build a representative benchmarking framework given business requirements for LLM.
- Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.

Deployment & Tools:
- Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.
- Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).
- Experience with cloud platforms (AWS, GCP or Azure) and big data technologies (Spark, Hadoop) for scaling data processing or model training is a plus.
- Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).
Leadership & Communication:
- Demonstrated ability to lead technical projects and mentor junior team members.
- Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies clearly.

Responsibilities:
- Lead end-to-end development of NLP and LLM models - from data exploration and model prototyping to validation and production deployment. This includes designing novel model architectures or fine-tuning state-of-the-art transformer models (e.g., BERT, GPT) to solve project-specific language tasks.
- Analyze large text datasets (Ukrainian and multilingual corpora) to extract insights and build robust training datasets.
- Guide data collection and annotation efforts to ensure high-quality data for model training.
- Develop and implement NLP algorithms for a range of tasks such as text classification, named entity recognition, semantic search, and conversational AI.
- Stay up-to-date with the latest research to apply transformer-based models, embeddings, and other modern NLP techniques in the solutions.
- Establish evaluation metrics and validation frameworks for model performance, including accuracy, factuality, and bias.
- Design A/B tests and statistical experiments to compare model variants and validate improvements.
- Deploy and integrate NLP models into production systems in collaboration with engineers - ensuring models are scalable, efficient, and well-monitored in a real-world setting.
- Optimize model inference and troubleshoot issues such as model drift or data pipeline bottlenecks.
- Provide technical leadership and mentorship to the NLP/ML team.
- Review code and research, uphold best practices in ML (version control, reproducibility, documentation), and foster a culture of continuous learning and innovation.
- Collaborate cross-functionally with product managers, software engineers, and MLOps engineers to align NLP solutions with product goals and infrastructure capabilities.
- Communicate complex data science concepts to stakeholders and incorporate their feedback into model development.

The company offers:
- Competitive salary.
- Equity options in a fast-growing AI company.
- Remote-friendly work culture.
- Opportunity to shape a product at the intersection of AI and human productivity.
- Work with a passionate, senior team building cutting-edge tech for real-world business use.

Client

Our client is a leading Fortune 500 financial technology company that provides comprehensive payment solutions and financial services across multiple continents. They process billions of transactions annually and serve millions of customers worldwide.

You'll collaborate with a world-class team of senior data scientists, ML engineers, and technology consultants from leading organizations in the fintech and cloud computing space. This diverse group brings together deep technical expertise, industry knowledge, and proven experience delivering mission-critical solutions at enterprise scale.

Position overview

We are seeking an experienced Senior Data Scientist with deep expertise in Generative AI implementations. This role is designed for seasoned data science professionals who have successfully transitioned their expertise into production GenAI environments - not for those simply exploring AI technologies.

Technology stack

AWS Bedrock, SageMaker, and comprehensive AI/ML service ecosystem
Vector databases and advanced RAG architectures
Enterprise-scale data processing and real-time model deployment systems
Automated CI/CD pipelines specifically designed for ML workflows

Responsibilities

• Design and implement data architectures for GenAI solutions across structured, semi-structured, and unstructured data sources
• Extract, prepare, and optimize data for consumption into AI platforms from data lakes and direct model ingestion
• Structure diverse data sources for proper ingestion into AI workflows and model training
• Develop and manage automated data streams and pipeline orchestration
• Collaborate with MLOps engineers to ensure seamless data flow for model training and inference
• Implement data quality monitoring and validation frameworks for GenAI applications
• Design feature engineering strategies specifically for Foundation Models and LLM implementations
• Scale proof-of-concepts to production-ready, enterprise-grade data solutions

Requirements

• Hands-on experience with diverse data sources (structured, semi-structured, unstructured) for AI platform integration
• Proven ability to extract, prepare, and structure data for consumption into AI platforms from data lakes or direct model ingestion
• Experience structuring various data sources for proper ingestion into AI workflows and Foundation Model training
• Advanced knowledge of automated data stream management and pipeline orchestration for AI/ML workloads
• Demonstrated experience building scalable data infrastructure supporting GenAI applications in production environments
• Strong background in AWS data services (S3, Glue, Kinesis, etc.) and integration with AI/ML platforms
• Advanced Python, SQL, and experience with big data technologies (Spark, Kafka, etc.)
• Proven track record of transitioning POCs to production-ready, enterprise-scale data solutions
• 5+ years data science experience with 2+ years dedicated GenAI data engineering and preparation experience
• Availability during US Eastern Time (ET) business hours to collaborate with onsite team

Nice to have

• Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, or related technical field (Master's preferred)
• AWS certifications (Data Analytics, Machine Learning Specialty, etc.)
• Experience with financial services or payment processing data systems
• Knowledge of data governance and compliance frameworks in regulated industries

Welcome to King Group - a place where the best people from the IT and gambling industries meet to do amazing things together. We operate numerous projects in the iGaming sector in the markets of Ukraine, Europe and the USA, invest in venture startups, promising ideas and people.
 

One of our companies is a game studio that deals with the full cycle of iGaming product development. From idea to release, we combine creativity, modern technologies and deep analytics to create a unique gaming experience. Our mission is to excite, inspire and shape the future of the industry.

Our company is looking for a math expert who has a drive and passion (perhaps some experience) for games.

Key skills (it's not necessary to have all key skills, but more is better than less):

• Higher education in mathematics or related fields (related to research, analytics or data processing);
• Have a strong math background (especially probability theory, statistics, combinatorics);
• Advanced knowledge of MS Excel (statistical, mathematical functions);
• Be proficient at one (at least) programming language (Python is preferable);
• Knowledge of one of the CAS (Mathematica, Mathcad, Maxima);
• Have a strong math background (especially probability theory, statistics, combinatorics);
• Have previous experience in gaming industry or (and) have experience in playing slots (or any other probabilistic games (poker, blackjack, etc));
• Understand the time and memory complexity of your code;
• Be keen on details (Yes, it's really important at this position);
• Understanding OOP Concepts;
• Experience in developing mathematical models.
   

Nice to have:

• Experience in using git;
• Experience in using JIRA.
   

Responsibilities: 

• Prepare math for slot games;
• Discuss with business/ propose new game ideas/new feature ides;
• Implement game logic of new features, games;
• Gather games' statistics by precise calculations / running simulations of the games;
• Make games attractive for players from the math side.
   

Why we:

• Social Package;
• Medical care;
• Sick Days;
• Professional development support;
• Family-like atmosphere. You can check it out yourself ;)
• Great career prospects.
  
  Do you want to grow with us? Do you have the desire to take an active part in creating a product? Send your resume and let's get to know each other ;)

В команду DataDiscovery шукаємо на розширення Senior Data Scientist. 

Наш ідеальний кандидат має:

- 5+ роки комерційного досвіду розробки.

Навички:

- Python та бібліотеки машинного навчання: TensorFlow, PyTorch;

- Технології Big Data: Kafka, Amazon S3, Spark;

- SQL та аналіз даних: робота з будь-якими джерелами даних (SQL, noSQL, векторні бази даних, column-oriented бази даних , тощо);

- Хмарні платформи: AWS, GCP;

Математична статистика: регресія, розподіл ймовірностей, - перевірка статистичних гіпотез тощо;

- Підходи машинного навчання: регресії, кластеризація, дерева рішень та інші;

- Алгоритми глибокого навчання: transformers, reinforcement learning, autoencoders, diffusion models, тощо;

- Досвід розробки продуктів AI: NLP, CV, Recsys, Generative AI;

- MLOps.

Що потрібно робити:

- Вирішувати продуктові та дослідницькі виклики крутого українського продукту;

- Працювати з реальними даними реальних користувачів;

- Вивчати та впроваджувати складні state-of-the-art алгоритми в області машинного навчання для вирішення практичних задач;

- Оцінювати технічні компроміси по кожному рішенню;

- Працювати в тісному співробітництві з іншими командами для дослідження нових можливостей використання інструментів AI.

Що ми пропонуємо:

- Роботу в стабільній компанії — адже ми понад 10 років на ринку;

- Дійсно цікаві завдання: бери участь у створенні медіасервісу майбутнього;

- Відносини, побудовані на довірі;

- Багато можливостей для розвитку;

- Неймовірно круті корпоративи;

- Безкоштовні уроки англійської мови;

- Заняття з плавання, а також уроки настольного тенісу;

- Корпоративного психолога;

- Для співробітників компанії знижки від брендів партнерів.

Відповідаючи на вакансію і надіславши своє резюме в Компанію (ТОВ «МЕГОГО»), зареєстровану й діючу відповідно до законодавства України, реєстраційний номер 38347009, адреса: Україна, 01011, місто Київ, вул.Рибальська, будинок 22 (далі «Компанія»), ви підтверджуєте та погоджуєтеся з тим, що Компанія обробляє ваші особисті дані, представлені у вашому резюме, відповідно до Закону України «Про захист персональних даних» та правил GDPR.

Один із провідних продуктових IT-холдингів України запрошує до команди Data Scientist!
Якщо тобі цікаво створювати ML-рішення, впроваджувати рекомендаційні системи та працювати з масштабними даними у професійному середовищі - будемо раді знайомству 🚀
 

Обов’язки:

• Розробка та навчання моделей машинного навчання (регресія, класифікація, кластеризація).
• Побудова й удосконалення рекомендаційних систем для бізнес-задач.
• Написання ефективного та масштабованого коду на Python.
• Написання та оптимізація SQL-запитів для отримання та аналізу даних.
• Співпраця з командою та дотримання кращих практик у версійному контролі (Git) та тестуванні.
   

Вимоги:

• 2+ роки досвіду у Data Science/Machine Learning.
• Впевнене володіння Python, SQL, Git.
• Глибоке розуміння методів ML (регресія, класифікація, кластеризація).
• Досвід розробки production-рішень з якісним та масштабованим кодом.
   

Буде плюсом:

• Досвід в побудові рекомендаційних систем.
• Знання тестування та MLOps-підходів.
   

Ми пропонуємо:

• Конкурентну зарплату та стабільні виплати.
• Медичне страхування.
• Гнучкий графік (старт з 8:00 до 11:00).
• L&D центр, self-learning бібліотека, доступ до внутрішніх та зовнішніх курсів.
• Програму ротації та кар’єрного розвитку.
• Підтримку ментального здоров’я (групові та індивідуальні сесії з психологом).
• Дружню команду експертів та продукти світового рівня.