Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

startup.jobs

Tech stack

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

Job description

• 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.

Requirements

• 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).

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