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- A dynamic high-tech company, combined with financial soundness and world-class investors. - Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment. - Lots of development opportunities as we continue to grow. - Challenging tasks and impactful projects alongside experts that enable professional and personal growth. - Corporate

Über das Unternehmen

Agile Robots is a leading provider of AI-powered robotics solutions. By combining artificial intelligence and robotics, the company makes industries smarter, more flexible, and more efficient. Founded in Munich in 2018 by renowned robotics researchers from the German Aerospace Center (DLR), Agile Robots has experienced rapid global growth.

Today, more than 3,500 highly skilled robotics and AI enthusiasts are employed worldwide. The company has one of the largest research and development teams in the AI and robotics industry. Agile Robots has built a unique portfolio; together with the subsidiaries BÄR Automation, Franka Robotics, idealworks, and Krause Automation, the company covers all areas of AI-driven robotics.

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Zur Position

The AI Teams at Agile Robots are looking for a Machine Learning Engineer (m/f/d) VLA Training & Evaluation, who will build the frameworks for fine-tuning and benchmarking robot foundation models on collected robot data. The frameworks are meant to work across different foundation models and for teams with modest data and compute. Responsibilities - Training Workflows: Build reusable, model-agnostic training and evaluation workflows, with experiment tracking, model versioning, and checkpoint management, so that different foundation models can be swapped in without rebuilding the pipeline. - Fine-Tuning: Implement fine-tuning of pretrained vision-language-action models on collected robot data, for example with parameter-efficient methods such as LoRA or adapters, or with distillation. - Policy Training: Build training loops for action-chunking and transformer-based action models, diffusion policies, and VLM/VLA heads. - Benchmarks: Build benchmarks with generalization splits, success metrics, and failure-analysis templates. - Transfer Studies: Work with simulation engineers on studies, including metrics and ablations, that test whether improvements in simulation realism improve performance on the real robot. - Research Baselines: Turn published methods into working baselines and templates that others can extend.

Essential Skills - Academic Background: Degree in Computer Science, Machine Learning, Robotics, or a related field (Master’s or PhD typical), or an equivalent combination of formal training and demonstrated experience training deep learning models. - Training Internals: Hands-on experience writing and debugging training loops in PyTorch or JAX, including data loading, loss design, and optimizer behavior. - Fine-Tuning: Experience fine-tuning pretrained models and adapting them to new tasks and data. - Robot Learning: Working understanding of how imitation-learned policies, such as action-chunking or diffusion policies, turn observations into robot actions. - Experiment Management: Experience designing evaluations and ablations, and running experiments with tracking, versioning, and checkpoint management so that others can reproduce them. Beneficial Skills - VLA Models: Experience fine-tuning vision-language-action or vision-language models for robot control. - Real-Robot Evaluation: Experience evaluating learned policies on physical robots. - Sim-to-Real: Familiarity with measuring how well models trained in simulation transfer to real hardware.