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 diffusion and flow-matching policies on demonstration data.
- Tune action representation and chunking on real hardware.
- Condition policies on visual history, proprioception, and language.
- Choose sampling to preserve action multimodality within the control budget.
- Recover from the off-distribution states a policy reaches by acting on prior outputs.
What we hope you'll bring
- Experience training and evaluating generative policies on real hardware.
- Knowledge of generative modelling for sequential decision problems.
- Publications in top machine learning or robotics venues, or equivalent experience.
- Fluency in Python and PyTorch.
- Experience with JAX or Rust.
- 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.
- Total compensation: CHF 120,000 - 170,000.
- Every full-time hire receives stock options as part of the stated total compensation.
Deadline to apply: None. Applications will be reviewed on a rolling basis.