Senior Machine Learning Engineer, Robot Learning (m/f/d)

Batch Robotics is revolutionizing how robots operate in factories — not through rigid programming or teaching by human demonstration, but with smart perception and AI intelligence. We are a team of 4 motivated people with +25 years of experience in industry and robotics. Our mission is to make robots smarter and more flexible, enabling batch-size-1 automation for surface treatment economically. Our technology helps factories in Europe to remain competitive with intelligent robotics.

We are now looking for a Senior Machine Learning Engineer with technical excellence and entrepreneurial drive to join us in developing the next generation of AI-driven robotic systems!

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.

Background: Master's Degree and more than 3 years of professional experience or a (almost) finished PhD in Robotics, Computer Science, or a related field.

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.