Jobs Data Science

Hello!

We are E-Com, a team of Foodtech and Ukrainian product lovers.

And we also break stereotypes that retail is only about tomatoes. Believe me, the technical part of our projects provides a whole field for creativity and brainstorming.

What we are currently working on:

• we are upgrading the existing delivery of a wide range of products from Silpo stores;
• we are developing super-fast delivery of products and dishes under the new LOKO brand.

We are developing a next-generation Decision Support Platform that connects demand  planning, operational orchestration, and in-store execution optimization into one unified Analytics and  Machine Learning Ecosystem. 

The project focuses on three major streams: 

•  Demand & Forecasting Intelligence: building short-term demand forecasting models, generating  granular demand signals for operational planning, identifying anomalies, and supporting commercial  decision logic across virtual warehouse clusters. 

•  Operational Orchestration & Task Optimization: designing predictive models for workload  estimation, task duration (ETA), and prioritization. Developing algorithms that automatically map  operational needs into structured tasks and optimize their sequencing and allocation across teams. 

•  In-Store Execution & Routing Optimization: developing models that optimize picker movement,  predict in-store congestion, and recommend optimal routes and execution flows. Integrating store  layout geometry, product characteristics, and operational constraints to enhance dark-store  efficiency. 

You will join a cross-functional team to design and implement data-driven decision module that directly  influence commercial and operational decisions. 

Responsibilities:

•  develop and maintain ML models for forecasting short-term demand signals and detecting anomalies  across virtual warehouse clusters;

•  build predictive models to estimate task workload, execution times (ETA), and expected operational  performance;

•  design algorithms to optimize task distribution, sequencing, and prioritization across operational  teams;

•  develop routing and path-optimization models to improve picker movement efficiency within dark  stores; 

•  construct data-driven decision modules that integrate commercial rules, operational constraints, and  geometric layouts;

•  translate business requirements into ML-supported decision flows and automate key parts of  operational logic; 

•  build SQL pipelines and data transformations for commercial, operations, and logistics datasets;

•  work closely with supply chain, dark store operations, category management, and IT to deliver  measurable improvements;

•  conduct A/B testing, validate model impact, and ensure high-quality model monitoring. 

Requirements:

•  bachelor’s Degree in Mathematics / Quantitative Economics / Econometrics / Statistics / Computer  Sciences / Finance; 

•  at least 2 years working experience on Data Science; 

•  strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques; 

•  proven experience with SQL (Window functions, CTEs, joins) and Python;

•  expertise in Machine Learning, Time Series Analysis and application of Statistical Concepts  (Hypothesis testing, A/B tests, PCA); 

•  ability to work independently and decompose complex problems. 

Preferred:

•  experience with Airflow, Docker, or Kubernetes for Data Orchestration; 

•  practical experience with Amazon SageMaker: training, deploying, and monitoring ML models in a  production environment; 

•  knowledge of Reporting and Business Intelligence Software (Power BI, Tableau, Looker); 

•  ability to design and deliver packaged analytical/ML solutions. 

What we offer

• competitive salary;
• opportunity to work on flagship projects impacting millions of users;
• flexible remote or office-based work (with backup power and reliable connectivity at SilverBreeze Business Center);
• flexible working schedule;
• medical and Life insurance packages;
• support for GIG contract or private entrepreneurship arrangements;
• discounts at Fozzy Group stores and restaurants;
• psychological support services;
• Caring corporate culture;
• a team where you can implement your ideas, experiment, and feel like you are among friends.

Description
 

Our platform supports AI-generated creatives and uses contextual signals, consent frameworks, and privacy-compliant data to select and serve ads.

We are looking for an experienced Data Scientist to work on Teza’s data-driven optimization layer. You will design and develop the intelligence that powers our data-driven decision-making.

Requirements
 

- BSc/MSc/PhD in Data Science, ML, Statistics, or related field
- Strong experience in ML modeling, feature engineering, and experimentation
- Experience with predictive modeling and ranking/recommendation systems
- Experience working with large-scale datasets (BigQuery, Spark, etc.)
- Strong Python skills
- Experience with embeddings, text classifiers, and LLM-based scoring
- Strong SQL skills

Responsibilities
 

- Build bidding AI models
- Analyze large-scale events data for data to drive decision-making
- Improve campaign selection, and safety classifier
- Develop AI metrics for real-time optimization
- Work closely with engineering, product, and AI teams on core logic

Would be a plus

- Experience in ad tech, marketing tech, or real-time bidding — big advantage
- Experience with reinforcement learning or bandits
- Knowledge of DSP/RTB systems
- Experience with fraud detection or brand safety
- Experience deploying ML models in production
- Experience with campaign optimization in Google/Facebook/RTB

We are seeking a Data Scientist with a strong foundation in physics, control theory, and mathematical modeling to join our team working on cutting-edge autonomous systems. The ideal candidate combines analytical rigor with practical experience in modeling, simulation, and algorithm development for autonomous platforms.

Levels: Middle and Senior (responsibilities and scope will be adjusted accordingly).

Key Responsibilities

• Develop and validate mathematical models for autonomous systems and dynamic environments.
• Apply data-driven approaches for system identification, optimization, and predictive control.
• Analyze large datasets from sensors and simulations to extract insights and improve system performance.
• Design and implement algorithms for control, navigation, and decision-making.
• Collaborate with cross-functional teams to integrate models into real-world autonomous platforms.

Required Qualifications

• 3+ years in R&D or applied data science/software development.
• Strong background in mathematical modeling, system identification, and control theory.
• Proficiency in Matlab/Simulink for modeling and simulation.
• Experience in signal processing and data analysis.
• Programming skills in Python and C++.
• Ability to quickly research and apply recent trends in control theory, autonomous systems, and data-driven modeling.
• Relevant work experience or education in STEM field

Nice to Have

• Knowledge of aerodynamics fundamentals.
• Experience with Machine Learning (e.g., reinforcement learning, predictive modeling).
• Familiarity with simulation tools such as Gazebo, AirSim.
• Hands-on experience with SITL/HITL testing.
• Exposure to flight control stacks like PX4, Betaflight, ArduPilot.

We are looking for a Junior Data Scientist eager to grow in the field of autonomous systems, with a focus on computer vision, control theory, and data-driven modeling. This role is ideal for someone with strong analytical skills and a passion for applying data science to real-world autonomy challenges.

Levels: Junior and Strong Junior (responsibilities and scope will be adjusted accordingly).

Key Responsibilities

• Assist in developing vision-based algorithms for perception and navigation.
• Support data analysis and sensor fusion for multi-sensor systems.
• Contribute to modeling and simulation tasks under guidance from senior engineers.
• Work with datasets from cameras, IMUs, and other sensors to extract insights.
• Stay up-to-date with recent research in computer vision, autonomy, and data science.

Required Qualifications

• 2+ years of experience in computer vision or data analysis.
• Understanding of geometric computer vision principles.
• Basic knowledge of control theory, signal processing, and data-driven modeling.
• Programming skills in Python and C++.
• Familiarity with Linux and single-board computers.
• Strong willingness to learn and adapt quickly.
• Relevant work experience or education in STEM field

Nice to Have

• Exposure to SLAM or Visual-Inertial Odometry (VIO).
• Familiarity with OpenCV, NumPy, and basic ML frameworks (PyTorch, TensorFlow).
• Knowledge of ROS2, Gazebo, or AirSim.
• Experience with PX4, Betaflight, or ArduPilot.
• Basic understanding of neural networks and CV frameworks.
• Interest in reinforcement learning or predictive modeling.

What is your new project?

• Domain: Cyber insurance
• Location: Israel
• Company size: 20+ employees
• Founded in: 2017

What makes this project exciting?

Our client enables insurers, brokers and agents to cyberprofile and benchmark the cyber insurance risk of small-medium businesses worldwide and to estimate the financial impact of cyber on their business using our unique Cyber Profiling technology. Their solutions for informed cyber underwriting, aggregated risk management, and digital customer enablement are used by leading companies in the US and Europe.

What makes you a great fit

• Proven experience as a Data Scientist
• Strong mathematical background with ability to design and validate risk models
• Good Python skills (Pandas & Numpy must have)
• Machine learning experience, ideally applied to risk modeling or insurance contexts
• Hands-on experience with Amazon SageMaker for building and deploying ML models
• Strong English for clear communication and progress updates
• Experience with cloud platforms, preferably AWS
• Understanding of databases, preferably MongoDB
• Experience with git, preferably GitHub

Nice to have:

• Knowledge and hands-on experience with AI and Large Language Models (LLM)
• Previous experience in insurance or financial risk modeling

Your day-to-day in this position

• Build and refine risk models using Amazon SageMaker, applying advanced machine learning techniques
• Work independently and solve problems without constant guidance
• Communicate progress and issues clearly in English
• Be hands-on and focus on practical implementation rather than management

As a Machine Learning Engineer, you'll work as part of the Wix CTO Office team, researching problems that can give Wix’s products a competitive edge across various challenges. A key focus of the team is on Agents over LLMs, exploring new techniques for building agents and developing innovative products that leverage them.  

In your day-to-day, you will:  

• Build POCs for research projects led by the team  
• Evaluate results and provide actionable insights  
• Collaborate with different teams at Wix to advance their agent implementations  

• Build shared infrastructure for agents  

   

Requirements

• Creativity and willingness to tackle ambitious, high-risk problems  
• 3+ years of experience in working on production code with active users  
• BSc in Computer Science or related field, MSc preferred  
• Proficient in Python; TypeScript is a significant advantage  
• Experience in training and evaluating Machine Learning models  
• Hands-on experience with building GenAI systems using LLMs and agents  
• Proven ability to work in a collaborative, cross-functional environment  
• Excellent written and verbal communication skills in English 

About the Team  

We are Wix's Data Science CTO Office team, a small group of researchers and engineers. We collaborate with various groups at Wix and the CEO on innovative research projects. Some projects aim to enhance Wix products with new features, while others focus on strategic research areas that can provide Wix with a competitive advantage.

About the Project

Our client is a leader in AI-powered performance marketing, operating across 25+ verticals with unmatched precision, speed, and scale. Their proprietary technology stack integrates seamlessly with major media platforms, enabling real-time event-level data exchange, optimization, and attribution.
At the core of their operation is a deep commitment to AI-driven decision-making. From real-time bidding engines and predictive lead scoring to campaign automation and anomaly detection, their in-house AI models are central to how we scale campaigns, reduce inefficiencies, and outperform market benchmarks.
They’ve built and continue to evolve a robust internal platform to empower media buyers, analysts, and operators with real-time alerts, smart recommendations, and semi-autonomous optimization tools.

Role Summary
‍Develops, trains, and evaluates ML models - especially tabular, predictive, and ranking models - and contributes directly to production-first modeling efforts across the funnel.

‍

Required skills:

• Strong hands-on experience with tabular ML models (XGBoost, LightGBM, CatBoost, logistic regression, etc.)
• Proven experience with data wrangling, feature engineering, dataset preparation
• Experience training, tuning, and evaluating models at scale
• Solid understanding of model validation
• Strong Python skills and familiarity with ML libraries
• Ability to translate hypotheses into measurable experiments
• Experience working closely with data engineering pipelines

Preferred Qualifications (Nice to Have)

• Experience with inference engineering: packaging models, building inference endpoints, optimizing latency
• Exposure to model monitoring: drift, data quality checks, performance monitoring
• Experience with containerization (Docker) and serving frameworks (FastAPI, Flask, TorchServe, Bento, etc.)
• Experience deploying ML models in production environments
• Prior experience with ranking, scoring or optimization models

Опис:

Our platform supports AI-generated creatives and uses contextual signals, consent frameworks, and privacy-compliant data to select and serve ads.
We are looking for an experienced Data Scientist to work on Teza’s data-driven optimization layer. You will design and develop the intelligence that powers our data-driven decision-making.
 

Вимоги:

- BSc/MSc/PhD in Data Science, ML, Statistics, or related field
- Strong experience in ML modeling, feature engineering, and experimentation
- Experience with predictive modeling and ranking/recommendation systems
- Experience working with large-scale datasets (BigQuery, Spark, etc.)
- Strong Python skills
- Experience with embeddings, text classifiers, and LLM-based scoring
- Strong SQL skills

Обов'язки:

- Build bidding AI models
- Analyze large-scale events data for data to drive decision-making
- Improve campaign selection, and safety classifier
- Develop AI metrics for real-time optimization
- Work closely with engineering, product, and AI teams on core logic

Буде плюсом:

- Experience in ad tech, marketing tech, or real-time bidding — big advantage
- Experience with reinforcement learning or bandits
- Knowledge of DSP/RTB systems
- Experience with fraud detection or brand safety
- Experience deploying ML models in production
- Experience with campaign optimization in Google/Facebook/RTB

Location: Remote (must overlap with EST business hours)
Contract Type: B2B
English Level: C1 (Advanced)
Compensation: Gross, TBD
Holidays: 10 public holidays (vacation & sick days unpaid)

About the Role:
We are seeking a talented Machine Learning Engineer with expertise in LLMs, computer vision, and multimodal data processing. You will develop and optimize models to extract insights, generate embeddings, and model relationships across text, images, and structured metadata. Your work will power similarity search, recommendation systems, and other AI-driven applications.

Core Responsibilities:

• Develop, fine-tune, and evaluate LLM and computer vision models for information extraction, embeddings, and relationship modeling across multimodal data.
• Build and refine vector embeddings for text, metadata, and visual representations to support similarity search and recommendation systems.
• Conduct systematic experiments to benchmark models, compare approaches, and improve accuracy, robustness, and retrieval quality.
• Define evaluation strategies for model performance, embedding quality, and search relevance.
• Prepare, clean, and analyze multimodal datasets from drawings, OCR outputs, and structured metadata.
• Perform feature engineering for visual, textual, and structured data used across ML models.
• Validate data consistency, identify issues, and propose improvements to labeling, preprocessing, and dataset structure.
• Collaborate on evaluating similarity search pipelines using Milvus, OpenSearch, or Elasticsearch.
• Analyze retrieval performance, investigate mismatches, and iterate on embeddings and preprocessing logic.
• Prepare trained models for deployment with clear documentation, evaluation reports, and defined inputs/outputs.
• Collaborate with engineering teams on inference pipelines and deployment strategies.

Monitoring & Continuous Improvement:

• Track model performance metrics (accuracy, recall, drift) and investigate error patterns.
• Recommend strategies for new data collection, labeling, or retraining to improve model quality.
• Support continuous improvement cycles driven by feedback and new data.

Collaboration & Documentation:

• Work with AI engineers, data engineers, QA, and business analysts to translate requirements into modeling tasks.
• Document datasets, experiments, and modeling decisions clearly.
• Communicate insights and results effectively to both technical and non-technical stakeholders.

Required Skills & Experience:

Machine Learning:

• Strong understanding of LLMs, computer vision, embedding models, and experimentation workflows.
• Ability to design and rigorously evaluate ML experiments.

Frameworks & Tools:

• PyTorch, TensorFlow, scikit-learn
• Hugging Face Transformers
• OpenCV, TorchVision

Programming & Data:

• Proficiency in Python for data manipulation, experimentation, and modeling
• Experience preparing and analyzing large-scale datasets
• Expertise in feature engineering and data quality assessment
• Familiarity with Milvus, OpenSearch, or Elasticsearch for similarity search
• Experience with AWS, S3, DynamoDB, EKS, Docker, Kubernetes
• Understanding of ML monitoring using OpenTelemetry, Prometheus, and Grafana

Collaboration & Communication:

• Strong ability to communicate modeling insights and data findings clearly
• Comfortable working cross-functionally with engineering, product, and business teams

Why Join Us:

• Work on cutting-edge AI and multimodal ML projects
• Collaborate with a talented, cross-functional team
• Opportunity to impact real-world applications with AI

We’re looking for an experienced ML Engineer / Data Scientist to join a product team building advanced AI systems using LLMs, computer vision, and multimodal data. You’ll work on everything from model training to embeddings, similarity search, benchmarking, and deployment.

What you’ll work on

• Build and fine-tune LLMs and computer vision models for information extraction and multimodal understanding.
• Create and optimize text/metadata/image embeddings for similarity search, clustering, and recommendations.
• Run experiments and benchmarks to improve model accuracy, robustness, and retrieval quality.
• Design evaluation strategies for embeddings, model performance, and search relevance.
• Prepare, clean, and analyze multimodal datasets (OCR, drawings, structured data).
• Work with vector databases and search engines (Milvus, OpenSearch, Elasticsearch).
• Collaborate on ML deployment, inference pipelines, and documentation.
• Monitor production model performance and support continuous improvement cycles.

Requirements

  Machine Learning

• Strong experience with LLMs, computer vision, embeddings, and ML experimentation.
• Solid understanding of evaluation methods for multimodal and retrieval tasks.

 Tech Stack

• Python, PyTorch / TensorFlow, scikit-learn
• Hugging Face Transformers, OpenCV, TorchVision
• Milvus / OpenSearch / Elasticsearch
• AWS (S3, DynamoDB, EKS), Docker, Kubernetes
• Familiarity with ML monitoring (OpenTelemetry, Prometheus, Grafana)

 Collaboration

• Ability to explain complex ML concepts to both technical and non-technical teams.
• Comfortable working closely with AI engineering, data engineering, and product teams.

Why this role?

• Work on cutting-edge multimodal AI (LLMs + CV + embeddings).
• High-impact role in a lean, engineering-driven environment.
• Opportunity to shape real-world applications powered by advanced ML.

We are looking for the first Data Science engineers to join our team and play a foundational role in building a private Large Language Model from the ground up. This is a rare opportunity to shape the technical direction, influence architectural decisions, and establish best practices for intelligent systems within the organization.

What you will do: 

• Design, build, and maintain classical machine learning models to support core product needs.
• Develop agent-based systems (LLM agents, multi-step agents, agent-to-agent workflows) as part of the broader LLM ecosystem.
• Conduct rigorous experiments and iterate quickly to validate approaches and improve system performance.
• Build and optimize inference pipelines, including performance tuning, monitoring, and alerting.
• Collaborate closely with Data Engineering to deploy models securely and reliably into production environments.

• Contribute to defining standards, workflows, and tooling for the new Data Science function.

   

What we expect:

• 3+ years of hands-on experience with Python.
• Strong SQL skills and understanding of data workflows.
• Solid grasp of machine learning methods and their practical applications.
• Experience building agents or agent-based systems is a strong plus.
• Initiative, ownership, and readiness to work in a greenfield environment where many solutions must be defined from scratch.

Description

About project: Mobile marketing and audience platform
 

Leading mobile marketing and audience platform, empowers the app ecosystem with cutting-edge solutions in mobile marketing, audience building, and monetization. With integration into over 500,000 monthly active apps and a global reach, our team leverages first-party data to deliver impactful and scalable advertising solutions.
 

We’re looking for a highly skilled, independent, and driven Machine Learning Engineer to lead the design and development of our next-generation real-time inference services - the core engine powering algorithmic decision-making at scale. This is a rare opportunity to own the system at the heart of our product, serving billions of daily requests across mobile apps, with tight latency and performance constraints.
 

Our platform empowers the mobile app ecosystem, simplifying mobile marketing, audience building, and mobile monetization. With direct integration into over 500,000 mobile apps, our platform processes enormous volumes of first-party data to drive intelligent, real-time decisions that fuel growth for our partners.
 

You’ll work at the intersection of machine learning, large-scale backend engineering, and business logic, building robust services that blend predictive models with dynamic, engineering logic - all while maintaining extreme performance and reliability requirements.

Job Description:
 

· Own and lead the design and development of low-latency Algo inference services handling billions of requests per day

· Build and scale robust real-time decision-making engines, integrating ML models with business logic under strict SLAs

· Collaborate closely with DS to deploy models seamlessly and reliably in production

· Design systems for model versioning, shadowing, and A/B testing at runtime

· Ensure high availability, scalability, and observability of production systems

· Continuously optimize latency, throughput, and cost-efficiency using modern tooling and techniques

· Work independently while interfacing with cross-functional stakeholders from Algo, Infra, Product, Engineering, BA & Business.
 

Requirements
 

· B.Sc. or M.Sc. in Computer Science, Software Engineering, or a related technical discipline

· 5+ years of experience building high-performance backend or ML inference systems

· Deep expertise in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML)

· Experience with scalable service architecture, message queues (Kafka, Pub/Sub), and async processing

· Strong understanding of model deployment practices, online/offline feature parity, and real-time monitoring

· Experience in cloud environments (AWS, GCP, or OCI) and container orchestration (Kubernetes)

· Experience working with in-memory and NoSQL databases (e.g. Aerospike, Redis, Bigtable) to support ultra-fast data access in production-grade ML services

· Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry) and best practices for alerting and diagnostics

· A strong sense of ownership and the ability to drive solutions end-to-end

· Passion for performance, clean architecture, and impactful systems

Interview process - 2 stages (technical and interpersonal).

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the 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's platform is designed to work with sales representatives and automate as much of their work as possible, in order to allow them to connect with their clients and make their communications faster. The company also provides intelligence to team managers with performance insights, allowing them to coach their sales reps more effectively.

About the role:
As a Machine Learning Engineer in the Conversational Intelligence Team, you will be responsible for building systems that process real-time audio and text streams from the Knowledge AI Assistant. You will build scalable machine learning platforms and systems to handle large quantities of data. Engineer low-latency and high-throughput ML systems that must scale for a growing business

Responsibilities:
- Working with voice streams and real-time transcriptions of voice streams to extract intelligence and actionable insights for customers
- Analyzing the relevance of extracted intelligence, performance of the extraction algorithms, and evaluating the performance of third party voice processing APIs
- Finding ways to engineer high availability into a system that has lots of moving parts, not all of which are directly under your control
- Working with stakeholders to make sure our customers have the tools they need to be successful on our platform
- Finding quick ways to prototype and test possible solutions to large problems
- Looking across the stack for the best return on investment for any given pain point.

Requirements:
- 4+ experience in the Machine Learning sphere
- 3+ years of experience shipping production-quality code in Python or C++, along with strong software engineering skills
- 1-2+ years of experience working with Natural Language Processing.
- Experience with cloud-based data platforms and continuous-deployment projects
- Experience with implementing machine learning pipelines
- Willing to go above and beyond to help other team members
- Ability to prioritize details along a path of iterative delivery
- Ability to quickly ramp up new technologies and start contributing.

Desirable:
- Experience with Go
- Experience building platforms from scratch

We offer:
- Competitive salary and perks; annual satisfaction survey and performance review
- Paid vacation and 100% paid sick leave
- Work in a creative ,friendly environment using cutting-edge technologies
- Technical and product leadership opportunities

Now we’re looking for a Data Science Expert who’s not just great with numbers, but hungry to build real things — models, insights, and features — that go live and make a measurable difference.
 
🔧 What You’ll Be Doing:
 
 

• Own the design and development of machine learning models, forecasting systems, and lead scoring algorithms
• Turn messy, high-volume data into clear business signals and predictive insights
• Work cross-functionally with product, engineering, and growth to embed models into the product and decision-making
• Research and prototype data-driven features like NLP, recommender systems, churn prediction, etc.
• Shape the future of our data science strategy and help define scalable best practices
   

✅ What Makes You a Strong Fit:
 
 

• 5+ years of experience in data science / ML / analytics roles
• Expert in Python, Pandas, scikit-learn, and SQL
• Experience building and deploying ML models into production environments
• Strong product mindset — you're not just building models, you're solving real business problems
• Excellent communicator — able to collaborate across technical and non-technical teams
• English level: С1 (you can comfortably read documentation, join team calls, and communicate in writing)

 
Bonus if you have:
 
 

• Experience working in a startup or product-led tech company
• Background in real estate, SaaS, or growth-focused environments
• Exposure to cloud platforms (GCP, AWS), ML Ops tools, or business intelligence systems
   
  🚀 Why You’ll Love It Here:
   
• You’ll work at the core of a product-led, AI-powered startup where data drives every decision
• Your work will ship — fast — and impact thousands of users
• High autonomy, low bureaucracy: we trust you to own your domain
• Remote-first culture with flexible hours and async-friendly communication
• Salary (fixed base) ABOVE the market average.
• A smart, passionate team that moves fast, supports each other, and builds cool stuff
•  15 paid vacation days + 5 paid sick days annually.
•  Paid access to courses, books, and growth resources.
•  Real decision-making power and ownership of your results.

Job Description

• 3+ years of experience in computer vision or related fields.
• Strong knowledge of machine learning and deep learning techniques.
• Proficiency in programming languages such as Python and C++.
• Extensive experience with computer vision libraries and frameworks like OpenCV, TensorFlow, and PyTorch.
• Familiarity with image processing techniques and tools.
• Hands-on experience with neural networks, CNNs, and other advanced ML models.
• Experience with hardware integration for vision-based systems.
• Good understanding of best practices of software development (code reviews, TDD, Git, etc.)
• Advanced English (written and verbal) for daily communication with the customer.
  
  Would be a Plus:
• Understanding of real-time processing and optimization.
• Experience with edge AI deployment.
• Experience with optimization and inference libraries (ONNX, TensorRT, OpenVINO, etc.).
• QNX or Linux development.
• Nvidia Holoscan experience
•  

Job Responsibilities

• Contribute to the design, development, code review, and testing of computer vision algorithms and systems.
• Develop new features and improve existing functionality in vision-based projects.
• Work on the integration of computer vision solutions with third-party tools and hardware.
• Collaborate with cross-functional teams to deliver high-quality, compliant products.

• Stay updated with the latest advancements in computer vision and machine learning technologies.

   

Department/Project Description

As a Computer Vision Engineer, you will join a mature and senior team dedicated to developing cutting-edge computer vision solutions for various applications. Our projects range from advanced image processing to real-time vision systems, contributing to fields like medical devices, robotics, autonomous vehicles and others. We emphasize technical excellence and offer stimulating environment that encourages innovation and professional growth.