Research Engineer — Robot Learning

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About us Bleu Robotics is an AI company developing universal AI software for the deployment of advanced robots , humanoids, mobile manipulators, and industrial arms, in real factories. The convergence of humanoid robotics and modern AI is one of the most significant technical opportunities of the decade, and most of the work today still happens in research settings. We are building the software layer required to bring it into operational use.

We have a strong ambition to become the next Microsoft to run on every industrial humanoid. We have closed our pre-seed round with Top-tier fund to deploy our robot in Tier 1 manufacturing facilities. We are looking for deeply passionate scientists who can help us push the frontier and build a major lab in our physical AI.

Our vision To enable a robot to learn a new industrial task from a few demonstrations and operate autonomously, with under one hour between the start of the demonstration and the start of deployment. This objective shapes our roadmap and every technical decision we make. The role We are hiring Research engineer in Robot Learning to design the next generation of few-shot, data-efficient imitation learning algorithms powering our robots.

Your work will define how Bleu Robotics teaches robots new industrial tasks — and how fast, from how little data, and how reliably. The central research challenge is data efficiency : learning useful, generalizing behaviors from a handful of demonstrations, not thousands. Hitting our one-hour demonstration-to-deployment target depends directly on the algorithms you will design.

This is a frontier research problem with immediate, measurable industrial impact: every gain in sample efficiency translates into a faster, broader, and more useful product. This is a research role focused on shipping algorithms into a real product. We are an early-stage company and do not publish at this stage. You will work in close collaboration with the Vision & Perception and Integration teams to ensure the methods you develop run on real robots, in real factories.

Responsibilities Design, prototype, and evaluate few-shot imitation learning algorithms, with a primary focus on data efficiency Advance our internal state of the art in imitation learning, policy learning, and representation learning for control Run rigorous experiments on real robots and contribute to the data, evaluation, and infrastructure needed to do so Translate research prototypes into algorithms robust enough to ship as