Research Engineer

About the role

Human Computer Lab is a research lab building character robots that feel alive and responsive. Our first robot, LeLamp, explores a new category of consumer robotics where everyday objects become interactive and help us reshape our attachment to technology.

Our goal is to push the frontier of human-robot interaction by making technology more legible, emotionally intuitive, and human-centered. We are building the foundation for a new generation of robots designed for everyday environments.

What to expect

We're looking for an inventor who builds. Someone who gets an unlikely idea on a weekend, tries it, watches it fail, notices something surprising in the wreckage, and follows that instead - all the way to a model, a dataset, a working robot, and a write-up anyone can reproduce. Research is the inventing half: finding the question nobody has asked, trying the unexpected method first, and building the evaluation that tells you the truth. Engineering is the making half: turning the idea into something that runs on a real robot, in a real home, fast enough that a person never notices the machinery. Most people are strong at one. This role is for the person who can't stop doing both. It isn't bound to one discipline - the problem might be expressive motion this month, sensing or thermals next quarter, an inference stack or a new robot body after that. You'll work directly with the CEO and founding team, with real room to explore, on problems that ship to people's homes.

In this role, you will:

- Pick open problems across the system - motion, perception, sensing, actuation, thermals, inference, tooling - and take them from first idea to working artifact.

- Try the unlikely approach before the obvious one, kill it honestly when it fails, and use what the failure taught you.

- Build the evaluation before you trust the result: the metric, the probe, and the side-by-side that catch what averages hide.

- Create data where none exists - bootstrap it, synthesize it, curate it with taste - and know when synthetic data is misleading you.

- Ship end to end: the model, the dataset, the firmware, the demo, and the robot in the room. Make it fast and reliable enough for everyday life.

- Work across abstraction layers when the problem demands it — from a circuit or motor driver up to a trained model and the interaction on top of it.

- Write up what you tried, what failed, what the numbers say, and what you'd do next, so someone else can reproduce it.

- Use AI as a research collaborator - to map a search space, run experiments, and pressure-test your own ideas - and share your judgment in how you use it for novel approaches.

You may be a good fit if you:

- Have a body of self-directed work - projects, repositories, write-ups, videos, robots - that you started because you were curious, not because it was assigned. This matters more to us than any credential.

- Have taken at least one idea from “what if” to a working, measured artifact, end to end.