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
- Train policies on demonstration data and run benchmarks on real hardware.
- Build and characterise datasets behind a benchmark, including failure cases and coverage gaps.
- Run policy evaluations across task variations and analyse where success rates break down.
- Reproduce published manipulation results.
What we hope you'll bring
- Experience training neural networks end-to-end.
- Fluency in Python and PyTorch.
- Exposure to diffusion policies, transformers, or vision-language-action (VLA) models.
- Familiarity with reinforcement learning (PPO, SAC) or policy learning on demonstration data.
- 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.
- Duration: 3-6 months.
- Monthly salary: CHF 3,000.
Deadline to apply: None. Applications will be reviewed on a rolling basis.