About Baseline Robotics
Open source revolutionised how we write software, but its impact on robotics stays limited due to 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 the measured change in success rate.
- Reconcile imagined or simulated rollouts with hardware outcomes when transfer is poor.
What we hope you'll bring
- 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).
We encourage you to apply even if you do not believe you meet every qualification.
Details
- Location: Zurich, Switzerland, on-site 5 days a week.
- Total compensation: CHF 120,000 - 145,000.
- Every full-time hire receives stock options as part of the stated total compensation.
Deadline to apply: None. Applications will be reviewed on a rolling basis.