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# (Senior) Machine Learning Engineer, Robot Learning

- **Company:** Batch Robotics GmbH
- **Location:** München, Germany
- **Experience:** Expert
- **Salary:** €70,000.0 - €120,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Big Data, Python (Programming Language), Machine Learning, Robotic Automation Software, Software Engineering, Systems Integration, Reinforcement Learning, Agentic-AI, Information Technology, Transformer Architectures, Machine Learning Operations, Data Pipelines
- **Published:** October 24, 2026
- **Apply:** https://www.careerjet.de/jobad/ded0724bbe0cdfaf645406fc218e4df15d

## About the Role

We are now looking for a Senior Machine Learning Engineer with at least 3 years of full-time industry experience (or a PhD finishing within 4 months), technical excellence and entrepreneurial drive to join us in developing the next generation of AI-driven robotic systems!, Background (must-have): Either (a) a Master's degree in Robotics, Computer Science or a related field plus at least 3 years of full-time professional experience after graduation - internships, working-student jobs and thesis projects do not count towards these 3 years - or (b) a PhD in one of these fields that is completed or will be completed within the next 4 months.
Availability (must-have): You can start full-time in Munich (hybrid) within a maximum of 4 months.
Deep experience: Hands-on work with reinforcement learning (RL) and imitation learning within physics simulations.
Software Engineering: Strong proficiency in Python and ideally Rust; ability to write clean, modular, and performance-optimized code for distributed workloads using AI agents.
Real-World Mindset: Proven track record of deploying models on physical robotic manipulators and handling noisy, temporal, or large-scale datasets.
Soft Skills: Entrepreneurial drive, solution-oriented mindset, high agency and the ability to take direct responsibility in a fast-paced startup environment.

## Description

End-to-End ML Systems: Design, implement, and train state-of-the-art AI models for perception and control of robot arms.
Data Pipelines & ML Ops: Collect real-world data from factory deployments to optimize model training and update models in the field.
Simulation & Synthetic Data: Work and improve simulation-based tooling (e.g., NVIDIA Newton Physics Engine) for physical AI and scalable synthetic data generation.
Model Development: Build ML components across perception and policy pathways, including video models, multimodal encoders, world models, and diffusion/transformer architectures.
Systems Integration: Work with real-world, multimodal datasets (vision, force/torque, tactile) and ensure models operate reliably under real-time constraints on physical hardware.
Evaluation & Iteration: Test models in realistic environments, analyze failures, and refine for stability, latency, and robustness.