Member of Technical Staff, Reinforcement Learning

About Baseline

Open source revolutionised how we write software, but its impact on robotics stays limited by expensive hardware accessible only to well-funded labs. We run that hardware as shared infrastructure for the broader community, billed only for active compute. For a given task, policies are evaluated under similar conditions, giving researchers common ground for fair comparisons.

In this role you will

• 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 its measured change in success rate.
• Reconcile imagined or simulated rollouts with hardware outcomes on poor transfer.

What we hope you'll bring

• Trained reinforcement-learning systems in robotics, simulation, games, or agents.
• A command of policy optimization 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).

We encourage you to apply even if you do not believe you meet every qualification.

Details

• Location: Zurich, on-site 5 days a week.
• Annual salary: CHF 120,000 - 145,000.
• Every full-time hire receives stock options as part of the stated salary.
• Deadline to apply: None. Applications will be reviewed on a rolling basis.