Intern, Robot Learning

Baseline Robotics; Zürich; Vor Ort; CHF 30.000 - 36.000; Vollzeit

Zusammenfassung

Baseline Robotics in Zurich, Switzerland, is seeking a motivated ML intern to join our on-site team for a 3–6 month period.

You will train policies, build benchmarks, and run evaluations on real hardware using PyTorch and Python, with access to GPU-accelerated simulators.

Monthly salary is CHF 3,000, and the role offers hands-on experience with diffusion models, RL, and state-of-the-art robotics benchmarks.

Qualifikationen

• 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).

Aufgaben

• 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.

Kenntnisse

Neural networks training

Python

PyTorch

Reinforcement learning

Policy learning from demonstrations

Diffusion models

Transformers

Vision-language-action models

GPU-accelerated simulation

Tools

Isaac Lab

Newton

mjlab

Genesis

Jobbeschreibung

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.

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).

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.