Early-stage SF startup building synthetic data infrastructure for embodied AI and robotics.
Founding Research Engineer, Robot Learning
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You'll train and evaluate robot policies on generated physics-grounded datasets, iterating across simulation and real-world hardware. One morning you might debug a manipulation failure mode in a kitchen environment; by afternoon you're designing a new synthetic pretraining curriculum for visuomotor control. The goal is making synthetic data as reliable and inspectable as labeled human demonstrations—then making it far more scalable.
What they're looking for
- 6+ years in robot learning, reinforcement learning, or closely adjacent applied research
- Deep expertise in Python and PyTorch, with a track record of turning paper ideas into running systems
- Experience with sim-to-real transfer, policy architectures for manipulation or navigation, or synthetic data pipelines
- Comfort working with physical robot platforms and the messiness of real-world evaluation
- Published research or open-source contributions in robotics or embodied AI