Intern, Robot Learning

About Baseline

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, 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.