Research Engineer

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 Archive mafia. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to help reshape physical labor and work on problems that matter at civilizational scale, join us.

As a Research Engineer, you’ll work on multimodal sensing systems and sensor fusion research for embodied AI and robotics. This role sits at the intersection of robotics, perception, hardware systems, and machine learning, where your work directly shapes how multimodal data is collected, synchronized, fused, and used for downstream VLA training.

You’ll research emerging sensing technologies across RGB-D video, motion capture, IMUs, tactile sensing, audio, and wearable systems, and study how different sensor combinations impact robot learning, policy performance, and generalization. You’ll work closely with hardware, ML, and research teams to design experiments, evaluate new sensing stacks, and help define the next generation of multimodal robotics datasets.