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, 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.