Top Machine Learning Jobs in Poland

Lead the development of GenAI infrastructure, build ML platforms, contribute to LLM testing and deployment, and collaborate across teams to improve AI-driven experiences.

As an ML Engineer II, you will develop and improve core components of Zendesk's GenAI infrastructure, focusing on LLM applications, benchmarking frameworks, and orchestration tools for AI agents while collaborating with cross-functional teams.

The Machine Learning Platform Engineer will manage cloud infrastructure, support AI researchers, and deploy models in production, focusing on MLOps and CI/CD practices.

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

As a Senior Data Scientist, you will design and deploy machine learning models, drive ML experiments, optimize data processes, and collaborate with engineering teams on data architecture.

Develop and implement Generative AI applications, optimize data solutions, and advise clients while using MLOps practices and ensuring security standards.

As an AI/ML Specialist Solutions Architect, you will design scalable AI solutions, manage AI deployments, and collaborate with clients to enhance customer satisfaction.

Lead the development of LLM-powered solutions, ensuring scalability and security. Oversee AI system integration and collaboration with various teams.

Lead the design and development of algorithmic stages for cloud applications in geospatial big data. Collaborate with teams to integrate solutions and align product development with customer needs.

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.

Lead a team of engineers to develop ML solutions, focusing on LLMs and AWS cloud. Oversee projects, manage resources, and ensure technical execution.

Design and develop machine learning solutions, manage ML infrastructure, ensure MLOps best practices, and collaborate with cross-functional teams.

Lead a cross-functional team in developing and maintaining the MLOps infrastructure at Zyte. Responsibilities include managing distributed systems, optimizing model platforms, and overseeing team operations.

The Senior ML Engineer will enhance fine-tuning methodologies, research inference optimization techniques, and re-implement LLM architectures focusing on distributed training and efficiency.

Lead a team in developing AI solutions using ML and LLMs, manage projects, optimize models, and ensure cloud best practices.

Develop and integrate machine learning solutions for fraud prevention in transactions, analyze data using SQL and Python, and manage the complete lifecycle of ML solutions.

As a Research Engineer in ML, you'll optimize large-scale ML systems, collaborate with Research Scientists, enhance models, and implement algorithms in Python.

Join Snowflake's AI R&D team as a Software Engineering Intern, contributing to AI-powered products and collaborating on innovative projects. Gain experience in ML frameworks, distributed systems, and software development best practices.

Lead and manage high-performing teams developing ML/AI platforms, focusing on LLM infrastructure and collaboration across departments.

The Data Engineer will design and maintain data pipelines and infrastructure, support analytics and automation, and collaborate on ML/GenAI projects.

The Senior Machine Learning Engineer will design, build, and maintain MLOps systems, mentor other engineers, and optimize large-scale ML workflows.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.