Staff RL Engineer On-Robot Policy & Safety (Stock Options)

Baseline Robotics; Zürich; Vor Ort; CHF 120.000 - 145.000; Vollzeit

Benefits dieser Stelle

Stock options

Zusammenfassung

Baseline Robotics in Zürich, Switzerland, seeks a researcher to advance reinforcement-learning systems on real hardware. You will run post-training loops, design reward models, and implement on-robot safety constraints to prevent hardware damage.

The role emphasizes translating simulation insights to real outcomes, aligning policy improvements with measured success, and collaborating across ML and robotics teams. On-site, full-time with stock options.

Qualifikationen

• Experience training reinforcement-learning systems in robotics, simulation, games, or agents.
• Knowledge of policy optimisation and offline or off-policy reinforcement learning.
• Publications in top machine learning or robotics venues, or equivalent experience.
• Fluency in Python and PyTorch, and the ability to debug a learning system end-to-end.
• Experience with JAX or Rust.
• Experience with GPU-accelerated simulation tools (Isaac Lab, Newton, mjlab, Genesis).

Aufgaben

• Run post-training and reinforcement-learning loops for on-robot policy improvement.
• Design reward models from vision, language, and operator feedback.
• Implement on-robot safety constraints to halt or recover a trial before hardware damage.
• Post-train a reference policy and account for the measured change in success rate.
• Reconcile imagined or simulated rollouts with hardware outcomes when transfer is poor.

Kenntnisse

Reinforcement learning

Python

PyTorch

Publications in ML/robotics

JAX

Rust

GPU simulation

Tools

Isaac Lab

Genesis

mjlab

Newton

Jobbeschreibung

Baseline Robotics in Zürich, Switzerland, seeks a researcher to advance reinforcement-learning systems on real hardware. You will run post-training loops, design reward models, and implement on-robot safety constraints to prevent hardware damage.

The role emphasizes translating simulation insights to real outcomes, aligning policy improvements with measured success, and collaborating across ML and robotics teams. On-site, full-time with stock options.