Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.indeed.com

Tech stack

Agile Methodology Artificial Intelligence Extract Transform Load (ETL) Software Debugging Machine Learning Language Modeling Performance Tuning Management of Software Versions Pytorch Deep Learning Information Technology Software Version Control

Job description

• 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.

Requirements

• 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., * 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.

About the company

• 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 Benefits Program covering health, mobility, and learning for 100€ net per month.
• Modern office facilities with a rooftop terrace overlooking Munich, free drinks & fruits, and regular company events contribute to a good working environment

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