Intern, Robot Learning

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.

• 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.

• 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).

• Location:
  
  Zurich, Switzerland, on-site 5 days a week.
• Duration: 3-6 months.
• Monthly salary: CHF 3,000.

Final date to receive applications:
None. Applications will be reviewed on a rolling basis.