Machine Learning Engineer – Robot Learning (m/f/d)

RobCo GmbH; München; Hybrid; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading robotics company in Germany is looking for a Machine Learning Engineer specializing in Robot Learning. You will develop, evaluate, and deploy advanced learning methods tailored to robotic manipulation. The role involves collaboration with various teams, ensuring the integration of ML workflows into real hardware. A Master’s degree in a relevant field and strong skills in Python and ML frameworks are essential. This position offers a hybrid work model, flexible hours, and opportunities for technical growth.

Leistungen

Flexible hours

Cutting-edge equipment

High ownership and autonomy

Qualifikationen

• Experience training or evaluating ML models is necessary.
• Familiarity with robotics concepts or deploying ML on real systems required.
• Ability to analyze model behavior and communicate insights is crucial.

Aufgaben

• Research and evaluate robot learning methods.
• Train and fine-tune ML models using datasets.
• Collaborate with teams to ensure safety and reliability.

Kenntnisse

Hands-on experience with robot learning

Strong coding skills in Python

Experience with PyTorch or JAX

Ability to design experiments

Strong problem-solving mindset

Ausbildung

Master’s degree in Machine Learning, Robotics, Computer Science, Mathematics, Engineering, or a related field

Tools

ROS 2

Ray

Anyscale

Jobbeschreibung

Your Mission

As a Machine Learning Engineer – Robot Learning, you will help develop, evaluate, and deploy learning-based methods for real-world robotic manipulation. You will work at the intersection of machine learning, robotics, and systems engineering, adapting state-of-the-art research into robust, scalable capabilities that run on real hardware.

You will collaborate closely with robotics, autonomy, perception, simulation, and software teams, and you will own ML workflows from model design to deployment and testing on real robots.

Your Responsibilities

• Research, evaluate, and benchmark state-of-the-art robot learning methods (VLA models, diffusion policies, RL, imitation learning, visuomotor models)

• Adapt academic models for practical, real-world deployment on RobCo’s modular robots

• Train and fine-tune ML models using RobCo datasets and simulation data

• Integrate learned policies with perception, control, and robot runtime systems

• Build scalable training, evaluation, and data pipelines in collaboration with infrastructure teams

• Define clear performance metrics and build automated evaluation procedures in simulation and on real hardware

• Analyze model performance, identify regressions, and drive improvements

• Collaborate with robotics and autonomy engineers to ensure safety, reliability, and real-time performance

• Participate in research planning and contribute to technical decisions across the robot learning stack

• Share knowledge, support junior team members, and help shape internal ML best practices

Your Profile

• Degree in Machine Learning, Robotics, Computer Science, Mathematics, Engineering, or a related field (Master’s degree necessary)

• Hands‑on experience with robot learning, imitation learning, reinforcement learning, or deep learning

• Strong coding skills in Python and experience with PyTorch or JAX

• Experience training or evaluating ML models (university projects, internships, research labs, industry experience all count)

• Familiarity with robotics concepts or experience deploying ML on real systems (internship or lab experience is sufficient)

• Ability to design experiments, analyze model behavior, and communicate insights clearly

• Strong problem‑solving mindset and eagerness to work on real robots

• Bonus: familiarity with ROS 2, scalable training tools (Ray, Anyscale), or robotic datasets

Why us?

• Shape the robot learning capabilities of a next‑generation modular robotics platform

• Work with real hardware, simulation tools, rich datasets, and scalable ML infrastructure

• Research‑focused culture with direct real‑world deployment

• High ownership, autonomy, and strong technical growth opportunities

• Hybrid work model, flexible hours, and cutting‑edge equipment