Research Scientist - Robot Learning

Spaitial

München, Germany

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

Computer Vision Distributed Computing Environment Python (Programming Language) Machine Learning Open Source Technology Pytorch Decoding

Own the training pipeline for vision-language-action (VLA) and world-action models (WAM) end to end, from data to a policy running on a robot.
Contribute to setting the technical direction for embodied research at SpAItial.
Close the sim-to-real gap through domain randomization, system identification, and calibration, and build evaluation that predicts real-world transfer.
Adapt VLM backbones for control: encoder choice and adapter strategies, co-training.
Curate and weight the training mix across heterogeneous robot datasets, spanning differing embodiments, action spaces, and sensor setups.
Design action representation and decoding, including tokenization, chunking, diffusion, and flow-matching action experts.
Build the world-model components that predict future observations conditioned on action.
Run post-training: supervised fine-tuning onto target embodiments, and RL for robustness beyond demonstrations.

A PhD in robotics, machine learning, or computer vision with a robot learning focus, from the PhD alone or followed by industry experience.
Publications at top venues such as (CoRL, RSS, ICRA, IROS or CVPR, ICCV, ECCV, NeurIPS), open-source work, and/or deployed systems.
Deep experience with modern robot policy designs (VLA, WAM, diffusion), trained end to end rather than fine-tuned from a released checkpoint.
Strong imitation learning fundamentals, and familiarity with RL fine-tuning of pretrained policies.
Fluency with VLM backbones and how to adapt them for control.
Expert Python and PyTorch, with multi-node distributed training experience (FSDP or equivalent).