Backed by Y Combinator.
About the role
Evaluate how different sensor modalities impact VLA training and downstream robotics performance Work across RGB-D video, IMUs, motion capture, tactile sensing, audio, and wearable systems data
What they're looking for
- Master’s or PhD in robotics, computer vision, sensing systems, or related fields
- Published research in sensor fusion, perception systems, multimodal datasets, or embodied AI
- Strong technical intuition around hardware systems and robot learning
- Experience with RGB-D video, IMUs, motion capture, tactile sensing, or robotics systems
- Experience with reinforcement learning, real-world robot deployments, and how data impacts downstream policy performance
- Highly curious, execution-oriented, and comfortable operating from first principles
More about this role
Human Archive is a research lab backed by Y Combinator focused on modeling human embodied intelligence.
Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence — including the human hand, proprioception, and vision — remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself.
Founded by Stanford and UC Berkeley researchers, we are lean, deeply technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability.
The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity.
We are building the infrastructure to accelerate that transition by assembling the Human...
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