Top Machine Learning Jobs in Poland

As a Backend Engineer, you will develop and enhance labeling tools and backend systems for mapping at Waymo, collaborating with various teams and integrating machine learning models.

As an Applied Science Intern, you will work on diverse projects involving graph analysis, reinforcement learning, and search technology, collaborating with scientists and engineers.

The role involves developing machine learning systems for robotic applications, enhancing robot dexterity, and collaborating on innovative solutions. It requires strong problem-solving skills and experience with deep learning and computer vision.

Design and develop software solutions for AI/ML applications, propose new approaches to optimization problems, and collaborate with global teams in research and development.

As a Senior Software Engineer on the Car Experience team, you will guide backend development and machine learning data pipelines focused on enhancing rider experience in autonomous vehicles.

Develop and optimize the ML training framework in high-performance C++, implement new ops and layers, debug and tune model performance on Tenstorrent chips, collaborate with compiler and kernel teams, and support integration and productionization of real-world models.

Design and implement document intelligence pipelines; develop ML models; optimize data processing systems; establish evaluation frameworks; collaborate with experts.

The role involves conducting R&D in machine learning, particularly in model training and architecture development. Candidates should have a background in deep learning and experience in hands-on experiments with foundational models.

Design, build, and operate large-scale geospatial data pipelines and production ML/agent systems. Own data ingestion, distributed processing, model training, deployment, monitoring, and observability. Develop backend services and tooling, integrate agent frameworks, collaborate with product and customer teams to drive revenue and reliability.

The Machine Learning Engineer II will develop and optimize ML models, process large datasets, and integrate ML solutions while collaborating with peers and staying up to date on technologies.

Develop and maintain ML models, improve data flow, troubleshoot issues, and document outcomes. Collaborate in an agile international team.

As a Senior Machine Learning Engineer, you will design and deploy AI models, work on data pipelines, and enhance AI-driven retail solutions through collaboration with various teams.

Responsible for full-cycle recruiting for senior AI roles with a focus on sourcing talent in LATAM and Europe, managing pipelines, and closing candidates.

As a Senior Machine Learning Engineer, you will drive document parsing, improve algorithms, and mentor team members in computer vision tasks.

Design, implement, and deploy ML and AI solutions; architect AI systems; build and oversee data pipelines; collaborate with engineering teams; translate business needs to data problems; present results to stakeholders; follow Agile practices.

Lead the design and development of low-latency machine learning inference services, ensuring high availability and scalability, while collaborating with data scientists and cross-functional teams.

Develop and scale neural networks for source code tasks, create and improve methods for code generation, editing, and agent-based workflows, mentor colleagues, and produce publishable ML research results.

You will optimize ML infrastructure, automate ML pipelines, manage versioning, and collaborate with engineers to ensure model scalability and reliability.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

Lead large-scale ML projects overseeing their entire lifecycle. Influence technical decisions, design ML pipelines, and mentor other engineers.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

This role requires designing, developing, and maintaining scalable AI systems, integrating AI orchestration frameworks, and collaborating with cross-functional teams.

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.