Top Machine Learning Jobs in Germany

The Developer Relations role involves building community engagement, creating technical content, managing social media, and collaborating on product development at Prior Labs.

The Product Manager for AI Platform will define strategy, implement AI solutions, ensure compliance, and collaborate with cross-functional teams.

The role involves leading the development of robotic solutions in agriculture, fostering innovation, building teams, and reporting progress to management.

Intern will control robots via VR for data collection, label visual data, assist with basic debugging, and collaborate with a tech team.

Develop and implement advanced generative AI models, collaborate with cross-functional teams, and innovate existing models for improved performance and speed.

The Pricing Advisory professional will assist clients in enhancing pricing strategies through consultation, model development, implementation support, and training sessions, while managing stakeholder relationships and navigating challenges in pricing sophistication.

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.

The Senior Machine Learning Engineer will architect and deploy recommendation systems, leverage large-scale data, develop machine learning models, and collaborate with engineering teams to optimize solutions and improve key business metrics.

Lead the development of high-fidelity simulations and validation workflows for robotic systems, focusing on sensor simulation and mechanical modeling to ensure reliable performance in real-world and virtual environments.

The job involves researching and developing scalable, efficient ML systems at NVIDIA, requiring a Ph.D. background in related fields and strong programming skills.

As the Senior Expert/VP for Reinforcement Learning, lead the development of AI testing frameworks and validate RL systems in complex environments.

The role involves designing and maintaining data pipelines, optimizing software performance, and collaborating with teams on machine learning projects. The position also includes ownership of cross-team efforts and knowledge sharing across the organization.

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

The Senior Machine Learning Engineer will develop AI models for customer support, optimize pipelines, collaborate with cross-functional teams, and ensure high-performance deployment of ML solutions.

The role involves driving new AI software sales, engaging with executives, and leveraging industry expertise to close large enterprise deals.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

Support EEG and fNIRS data acquisition, quality control, and curation. Develop workflows, implement quality metrics, and prepare datasets for AI applications.

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

In this role, you'll engage with senior stakeholders to understand their business challenges, design AI-powered solutions, and guide customers through implementation and adoption, ensuring effective use of new workflows.

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.

As a Senior Forward Deployed Engineer, you'll deliver AI solutions, engage with customers, and lead project lifecycles, including deployment and integration.

Design, train, evaluate, and deploy end-to-end driving ML models; lead projects on personalization and collaborative driving; build evaluation pipelines and data curation tools; influence architecture, training, and deployment; collaborate cross-functionally and mentor engineers.

Lead development of production-scale deep learning driving models, build evaluation and data pipelines, drive architecture and deployment, mentor engineers, and collaborate cross-functionally to deliver personalized, robust autonomous driving features.

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.