Top Machine Learning Jobs in Berlin

Lead the development of a real-time recommendation engine in Ad Tech, optimizing machine learning solutions for high availability and low latency while mentoring engineers and collaborating with cross-functional teams.

The Senior ML Software Engineer will design and build scalable production-grade ML systems, support buying decisions, and collaborate with Applied Scientists and stakeholders to optimize assortment management.

Design and implement scalable machine learning systems, collaborate with product teams, and apply MLOps best practices in production environments.

As a Senior Machine Learning Platform Engineer, you will design and manage data and ML pipelines, implement CI/CD, and optimize cloud infrastructure, focusing on natural language processing.

As a Senior ML Researcher at JetBrains, you will train LLMs for software engineering agents, design experiments, and mentor junior colleagues.

The Senior Machine Learning Engineer will design evaluation frameworks, diagnose model performance, develop ML systems, and mentor junior engineers while collaborating across teams.

Intern will explore collaborative learning and differential privacy while developing efficient systems for large ML models, aiming for academic publication.

The intern will contribute to MLOps, design AI/ML systems, implement research ideas, and work on various optimization models, while gaining hands-on experience.

Design, build, and scale core infrastructure for machine learning models, manage production systems, and implement best practices in MLOps.

Lead the development of fraud prevention products, collaborating with technology and risk teams, analyzing trends, and ensuring compliance with regulations.

The AI Researcher/Engineer will develop AI agents to autonomously conduct scientific discovery, leveraging AI systems and scientific computing in materials research.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

As a Senior Machine Learning Engineer at Veeva Systems, you will build scalable AI tools, develop ML infrastructure, mentor data scientists, and collaborate across teams to enhance data products in the life sciences sector.

As an Applied ML Engineer, you will build and scale data pipelines for video generation, ensuring data quality and optimizing preprocessing workflows.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

The Senior ML Infrastructure Engineer will own and optimize GPU infrastructure for training tabular foundation models, ensuring high efficiency and collaboration with research teams.

As an ML Engineer, you will design sklearn-compatible APIs, manage PyTorch serialization, build preprocessing pipelines, and enhance developer experience in the tabular model ecosystem.

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.

As an ML Engineer Intern, you will build and deploy ML models, design scalable infrastructure, and collaborate with data science teams in a fast-paced environment.

As an ML Engineer, you'll build and deploy scalable ML systems, optimize models, and collaborate with teams to improve content across platforms.

Develop and maintain data-driven systems focusing on real-time feature engineering and MLOps practices while collaborating with Machine Learning and Data Science teams.

The Senior Machine Learning Engineer will develop and maintain high-performance ML systems, enhance MLOps capabilities, and collaborate with cross-functional teams to improve marketplace performance.

Support and improve ML/LLMOps and AI-agent infrastructure: evaluate LLM frameworks and APIs, build RAG pipelines, design agent evaluation systems, automate ML/data pipelines, and document findings.