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Reinforcement Learning Research Scientist for Dexterous Manipulation (human)
Neura Robotics —Metzingen
Foundation Models: Fine‑tuning VLA policies with deep reinforcement learning to achieve highly dexterous, simulation‑driven manipulation.
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Reinforcement Learning Research Scientist for Dexterous Manipulation (Mensch)
Neura Robotics GmbH —Metzingen
Hier bist du Teil eines Teams, das technologische Grenzen verschiebt und eng mit anderen Bereichen wie Software, Hardware und Produktmanagement zusammenarbeitet…

data scientist jobs near holzmaden

Together, we are taking the step into a new era of cognitive robots:

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, RoboCasa, and related large-scale test suites.
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.

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