Jobs Data Science

Computer Vision engineer

DeepX is looking for a Computer Vision engineer

Core requirements:
- Python development experience
- Knowledge of popular data processing libraries (numpy, pandas, scipy etc)
- GIT, Linux, bash

Nice to have (or able to learn quickly):
- Hands-on experience with popular Machine Learning frameworks (OpenCV, Pytorch, Tensorflow, Keras, Yolo)
- Understanding of Computer Vision problem space
- Machine Learning and Artificial Neural Networks fundamentals
- Linux, Docker, gcc/make etc experience
- C++ and C, creating Python C extensions

A bit about DeepX company:
DeepX is an R&D intensive and innovation-driven consortium that provides Artificial Intelligence-powered Computer Vision solutions for businesses.
Please, take a look to find out more about us https://deepxhub.com/
We are a group of enthusiastic adherents who are passionate about the idea of improving this world’s safety by making Computer Vision technologies more advanced. Lifetime learning is our motto, we are admire ambition hard workers who are ready to face new challenges. If you feel like we might be on the same page, please, apply!

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

We are looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.

Responsibilities: 

• Design, implement, and optimize classical CV algorithms independently
• Develop and maintain components of SLAM/VIO pipelines
• Contribute to sensor fusion modules involving visual and inertial data
• Lead experiments, evaluate model performance, and propose improvements
• Review research papers and translate findings into practical solutions
• Collaborate across teams to ensure smooth integration of CV components.

Required Qualifications:

• Strong practical experience with classical CV and geometric vision
• Hands-on experience developing components of SLAM and/or VIO systems
• Proficiency in C++ and confident working in Linux environments
• Solid understanding of multi-sensor systems and data fusion
• Proven ability to prototype, benchmark, and optimize CV algorithms
• Ability to analyze research papers and implement state-of-the-art methods.

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems

Role Summary

We are looking for a Senior Data Scientist to lead the data intelligence layer of an AI-driven platform. This role focuses on extracting insights from structured and unstructured documents, building document processing and OCR pipelines, improving semantic retrieval systems, identifying information gaps in datasets, and maintaining high standards of data quality across end-to-end data workflows.
 

Key Responsibilities

• Design and improve OCR and document intelligence pipelines that convert complex PDFs and supporting files into structured, machine-readable data.
• Develop semantic retrieval workflows that identify relevant historical records and support recommendation systems based on similarity and contextual matching.
• Create data-driven methods to detect missing, incomplete, or inconsistent information within document-based datasets.
• Define and monitor data quality standards, validation rules, completeness checks, and anomaly detection mechanisms across data pipelines.
• Analyze structured and unstructured datasets to uncover patterns affecting operational performance, classification quality, and decision outcomes.
• Collaborate with machine learning engineers to prepare high-quality datasets for model training, validation, and iterative improvement.
• Develop labeling strategies, feature definitions, and dataset enhancement workflows for early-stage and production use cases.
• Evaluate the performance of retrieval systems, document extraction pipelines, and information coverage.
• Work closely with engineering and product teams to operationalize insights through dashboards, tools, and data-driven workflows.
• Communicate analytical findings, experiment results, and actionable insights clearly to technical and non-technical stakeholders.
   

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Artificial Intelligence, or a related technical field.
• 5+ years of experience in data science, applied NLP, document intelligence, or analytics-focused roles. 
• Strong proficiency in Python and practical experience with data science libraries and NLP tooling. 
• Experience building document processing or OCR pipelines and working with semi-structured or noisy datasets.
• Strong understanding of embeddings, semantic search, retrieval evaluation, and similarity-based recommendation systems.
• Experience with data cleaning, validation, feature engineering, and dataset quality management.
• Ability to design experiments and translate analytical findings into product or model improvements.
• Strong SQL skills and solid analytical problem-solving abilities.
   

Nice to Have

• Experience working with AI systems in regulated or compliance-sensitive environments.
• Familiarity with multilingual NLP applications or systems that support multiple languages.
• Experience with vector databases or retrieval-augmented architectures.
• Exposure to annotation workflows and human-in-the-loop review processes.
• Experience building dashboards, defining operational KPIs, or supporting data-driven reporting.
  
  We offer:
• Flexible working schedule
• Fully remote work
• Compensation for medical, educational and sports activities
• Professional growth opportunities
• Internal knowledge-sharing talks
• Awesome team events and activities
• Paid vacation and sick leave

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the client and role:
We are looking for a Computer Vision/3D Vision Engineer for our client, who is the creator of the world’s leading patented mobile body scanning technology, driving retail innovation by providing personalized fit and size recommendations and virtual try-on solutions for shoppers. The company helps brands reduce returns while simultaneously increasing conversion rates and average order value (AOV).

Responsibilities:
- Design, train, and deploy computer vision systems from initial dataset preparation to production-ready models.
- Solve complex problems in object detection, segmentation, and pose estimation using custom-built solutions.
- Develop and optimize pipelines for 3D reconstruction, depth estimation, and point cloud processing.
- Evaluate and debug model performance, ensuring high accuracy and technical depth in every solution.
- Manage dataset annotation, curation, and model evaluation metrics to ensure robust performance.

Requirements:
- Hands-on experience with object detection, segmentation, tracking, and camera calibration. You should have a deep understanding of image geometry.
- Proficiency in at least one of the following: 3D reconstruction, Multi-view geometry, Photogrammetry, NeRF/Neural rendering, or SLAM.
- Expert-level knowledge of Python and frameworks like PyTorch or TensorFlow.
- Proven ability to build and explain a CV system end-to-end, including architecture choice and deployment logic.
- Critical thinking and the ability to understand why a model works (or doesn't), with a focus on experimentation and debugging.

We are looking for a Data Scientist to join the analytics team of a leading Life Sciences / Pharma insights agency. The role focuses on advanced data modelling, NLP analysis, and transforming complex datasets into meaningful insights for pharmaceutical clients.

This is a hands-on role for someone who enjoys working with real-world datasets, building models, and communicating insights to stakeholders in the healthcare and scientific space.

The position is remote, full-time, and initially offered as a 6-month contract with strong potential extension.

ROLE OVERVIEW

You will work closely with analysts, researchers, and client teams to develop data science solutions that support insight generation for pharmaceutical and healthcare projects.

The role combines advanced modelling, NLP analysis, and data analytics, with an emphasis on interpreting complex data and presenting results clearly to non-technical stakeholders.

KEY RESPONSIBILITIES

Data Science & Modelling

• Develop machine learning and statistical models to analyse structured and unstructured data
• Apply NLP techniques such as topic modelling, clustering, text classification, and sentiment analysis
• Work with large datasets from surveys, research, and digital sources

Advanced Data Analysis

• Perform exploratory data analysis and statistical investigations
• Identify patterns, trends, and meaningful signals in complex datasets
• Translate modelling results into actionable insights

Data Processing & Pipelines

• Build and maintain data processing workflows
• Work with ETL pipelines and data transformations
• Ensure data quality, validation, and consistency

Insight Communication

• Present analytical findings clearly to internal teams and clients
• Build visualisations and reports that communicate complex results effectively
• Collaborate with analysts and consultants to shape research outputs

AI & LLM Applications

• Experiment with LLMs and AI tools for text analysis, summarisation, and insight generation
• Use modern AI-assisted development tools to accelerate workflows

REQUIRED SKILLS

• Strong Python skills for data science and analytics
• Experience with machine learning and statistical modelling
• Practical experience with NLP techniques (topic modelling, embeddings, classification, clustering)
• Experience with data analysis libraries such as pandas, NumPy, scikit-learn
• Ability to work with messy real-world datasets
• Strong analytical thinking and problem-solving ability

• Ability to clearly explain complex analytical results

   

NICE TO HAVE

• Experience working in Pharma, Life Sciences, or Healthcare analytics
• Experience working in agencies or consulting environments
• Familiarity with LLMs or AI-assisted data analysis tools
• Experience with data visualisation tools (e.g. Tableau, Power BI, Python visualisation libraries)

IDEAL CANDIDATE

You are someone who:

• enjoys building models and analysing complex datasets
• understands how to turn modelling results into insights
• can communicate findings clearly to non-technical audiences
• is comfortable working in a fast-paced consulting environment

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:
The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Our mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

About the role:
We are seeking an experienced and motivated Senior Data Scientist to join our dynamic team. The ideal candidate will have deep expertise in supervised learning, reinforcement learning, and optimization techniques. You will play a pivotal role in developing and implementing advanced machine learning models, driving actionable insights, and optimizing our advertising solutions.
This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

Responsibilities:
- Develop and implement advanced supervised and reinforcement learning models to improve ad targeting and campaign performance.
- Collaborate with cross-functional teams to identify opportunities for leveraging machine learning and optimization techniques to solve business problems.
- Conduct extensive data analysis and feature engineering to prepare datasets for machine learning tasks.
- Apply optimization algorithms to enhance the effectiveness and efficiency of advertising campaigns.
- Evaluate and refine existing models to enhance their accuracy, efficiency, and scalability.
- Utilize statistical techniques and machine learning algorithms to analyze large and complex datasets.
- Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
- Stay updated with the latest advancements in machine learning, reinforcement learning, and optimization techniques.
- Work with engineering teams to integrate models into production systems.
- Monitor, troubleshoot, and improve the performance of deployed models.
- Mentor junior data scientists and contribute to the continuous improvement of the data science practice within the company.

Requirements:
- 5+ years of experience in data science or machine learning roles, with a strong focus on supervised learning, reinforcement learning, and optimization techniques.
- Technical Skills:
- Proficiency in Python.
- Strong understanding of working with relational databases and SQL.
- Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
- Deep understanding of statistical modeling and supervised learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests, SVMs, gradient boosting, neural networks).
- Hands-on experience with reinforcement learning algorithms and frameworks like OpenAI Gym.
- Practical experience with optimization algorithms (linear, non-linear, combinatorial, etc.).
- Hands-on experience with data manipulation tools and libraries (e.g., pandas, NumPy).
- Familiarity with cloud services, specifically AWS, is a plus.
- Practical experience building and managing cloud-based ML pipelines using AWS services (e.g. SageMaker, Bedrock) is a plus.
- Education:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. A PhD is a plus.

Other Skills:
- Strong analytical and problem-solving skills.
- Excellent communication skills, with the ability to clearly articulate complex concepts to diverse audiences.
- Ability to work in a fast-paced environment and manage multiple priorities.
- Strong organizational skills and attention to detail.
- Ability to mentor and guide junior data scientists.
- Must be able to communicate with U.S.-based teams

The company offers:
- An opportunity to be at the forefront of advertising technology, impacting major marketing decisions.
- A collaborative, innovative environment where your contributions make a difference.
- The chance to work with a passionate team of data scientists, engineers, product managers, and designers.
- A culture that values learning, growth, and the pursuit of excellence.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

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.

As part of the AI transformation strategy, Kyivstar invests resources into development of products and services that uses cutting edge ML and Speech technologies to significantly improve internal processes and day-to-day tasks, but also uses gained experience to help partners and clients to receive maximum benefits from these technologies too.

Necessary skills:

•4+ years of experience in Machine Learning, Deep Learning and Speech Processing

•Strong Python skills with experience building maintainable and production-ready ML code

•Fundamental knowledge of  ML/DL theoretical concepts

•Hands-on experience with training and deploying models for various speech processing tasks including data preparation (e.g., speech classification, speech-to-text, text-to-speech, speaker diarization, etc.)

•Practical experience developing and deploying end-to-end production-ready ML pipelines, covering data gathering, preparation, training, validation, and inference

•Proficient in version control (Git) and containerization tools (Docker)

Would be a plus:

•Experience with real-time / low-latency speech systems

•Experience with distributed data processing and training in multi-node, multi-GPU environments

•Familiarity with PySpark for large-scale data processing

•Experience with Azure/AWS

•Exposure to Data Science in the Telecom industry

Responsibilities:

•Design, train, and optimize ASR and TTS models for production use

•Design, train, and evaluate classical ML models

•Perform feature engineering and data analysis for both speech and non-speech data

•Collaborate with DE/DevOps team to deploy the solution

•Plan and manage ML projects, articulate methodologies, and communicate results and business impact to stakeholders and customers

Proposed:

•Working with huge amounts of data at the largest operator in Ukraine

•Hands on experience with Cloud technologies and tools

•Great diversity of tasks and projects to utilize your knowledge and skills; flexibility in tools and approaches

•Opportunity to grow within the team of talented data scientists

We are looking for a Autonomy Engineer to work on 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 experience applying data science to real-world autonomy or robotics problems.

Key Responsibilities

• Develop vision-based algorithms for perception and navigation.
• Work on data analysis and sensor fusion for multi-sensor systems.
• Build models and simulations to support system development and testing.
• Process and analyze datasets from cameras, IMUs, and other sensors to improve autonomy performance.
• Collaborate with robotics and software engineers on perception and navigation pipelines.
• Stay up to date with recent research in computer vision, autonomy, and data science.

Required Qualifications

• 2+ years of experience in data science, robotics, computer vision, or a related field.
• Knowledge of control theory, PID controllers, signal processing, and data-driven modeling.
• Understanding of geometric computer vision principles.
• Strong programming skills in Python and good working knowledge of C++.
• Experience working with Linux-based systems.
• Degree in Computer Science, Robotics, Engineering, Mathematics, or another STEM field (or equivalent practical experience).

Nice to Have

• Experience with SLAM or Visual-Inertial Odometry (VIO).
• Familiarity with OpenCV, NumPy, and ML frameworks such as PyTorch or TensorFlow.
• Experience with ROS2, Gazebo, or AirSim.
• Experience with PX4, Betaflight, or ArduPilot.
• Understanding of neural networks for computer vision.
• Interest in reinforcement learning or predictive modeling.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

DeepX is looking for a Computer Vision Engineer to develop high-precision 3D reconstruction systems based on polarization imaging.

In this role, you will work at the intersection of computer vision, optical physics, and geometry, building algorithms that extract detailed surface information from polarimetric data using Shape-from-Polarization (SfP) techniques.

Core requirements:

● Strong understanding of polarized light physics and Stokes vector representation;

● Solid knowledge of linear algebra and vector calculus for coordinate transformations and 3D geometry;

● Experience implementing algorithms in Python (NumPy, SciPy);

● Practical experience with computer vision libraries such as OpenCV or scikit-image;

● Ability to extract reliable signals from noisy imaging data.

Nice to have:

● Experience with Shape-from-Polarization (SfP) or other Shape-from-X methods;

● Understanding of surface normals, depth maps, and 3D reconstruction pipelines;

● Experience working with polarization cameras;

● Knowledge of light reflection and scattering on different materials;

● Experience with normal integration methods for surface reconstruction.