Reinforcement Learning Research Scientist for Dexterous Manipulation (human)

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Stellenbeschreibung:

Reinforcement Learning Research Scientist for Dexterous Manipulation (human)

Neura Robotics • Metzingen

Give Our Robots Consciousness

In the AI Department, you'll develop the cognitive abilities of our robots, enabling them to understand and interact with their surroundings. You'll work on algorithms that make machines intelligent and adaptive—ranging from object recognition and language processing to decision‑making in complex environments. You'll be part of a team that pushes technological boundaries and collaborates closely with software, hardware, and product management teams. Using cutting‑edge tools and methods from machine learning and AI, you'll work on projects that have a direct impact on our products. If you're excited about making machines smarter and shaping the future of human‑machine interaction, the AI Department is the perfect place for you.

Your mission & challenges

Advanced AI for humanoid robotics: Design, train, and deploy next‑generation learning‑based policies that enable humanoid robots to perform dexterous manipulation and coordinated whole‑body behaviors in the real world.
Foundation Models: Fine‑tuning VLA policies with deep reinforcement learning to achieve highly dexterous, simulation‑driven manipulation.
End‑to‑end RL pipelines: Build complete reinforcement learning systems, from data generation and environment design to large‑scale training, evaluation, and deployment on physical robots.
State‑of‑the‑art learning methods: Advance reinforcement learning, imitation learning, and sim‑to‑real transfer to enable scalable, reliable humanoid behavior.
Benchmark‑driven quality: Design and evaluate robotic policies using modern manipulation benchmarks such as CALVIN and RoboCasa.
Deep hardware collaboration: Collaborate closely with hardware and control teams to seamlessly integrate your models into real robots.
From simulation to real robots: Validate and iterate on algorithms through real‑world experiments, closed‑loop testing, and full sim‑to‑real deployment.

What we can look forward to

An excellent Master’s or PhD in Computer Science, Informatics, Robotics, Physics, or a related field.
A proven track record: Your projects, patents, and open‑source or research contributions demonstrate measurable impact.
The desire to go beyond the state of the art – you don’t just want to improve, you want to create something new.
Strong foundation in deep reinforcement learning, imitation learning, and modern ML architectures.
Experience developing and fine‑tuning multimodal/VLA models, including RL for embodied agents.
Proven ability to build scalable training and deployment pipelines for real‑world robotic systems.
Expert programming skills in Python and C++, with PyTorch or JAX, focused on performance and rapid experimentation.
Hands‑on experience with advanced physics simulators (Isaac, MuJoCo, Newton, etc.).
Practical sim‑to‑real expertise, including system identification and robust domain transfer.
Direct experience with robotic hardware, multisensor systems, and manipulation tasks.
Ability to execute quickly, take ownership, and thrive in fast‑paced environments.
Strong communication skills across research, engineering, hardware, and product teams.
Bonus strengths: knowledge of foundation models (flow/diffusion), differentiable simulators, top‑tier publications, and open‑source contributions.