Product Manager - Humanoid Platform

About Flexion: At Flexion, we're building the intelligence layer powering the next generation of humanoid robots. Our mission is to accelerate the transition from fragile prototypes to real-world deployment of humanoids. We are founded by leading scientists in robot reinforcement learning (ex-Nvidia, ex-ETH Zürich) and backed by leading international VC firms. Within a few months, we’ve gone from our first line of code to deploying real humanoid capabilities with our partners. The Role: We are looking for an exceptional engineer whose interest goes beyond writing code to caring about taste: what feels intuitive, what's worth building versus skipping, and what the right abstraction looks like when there's no existing playbook to copy. We build software, not robots. Our autonomy stack runs on humanoids designed and manufactured by others, which makes getting it onto each new machine, and keeping it working there, a product problem. That problem is yours. You'll join the product team, sitting at the intersection of our infrastructure and AI teams and our customers, owning everything that touches the customer's physical robot: the tools developers use to bring our autonomy software onto a new platform, the work of characterizing that platform accurately enough for our controllers to transfer to it, and the tooling that keeps deployed robots observable and updatable in production. You'll spend real time in our testing space with robots, travel to customer sites to understand integration pain points firsthand, and translate that into a roadmap that accelerates every subsequent deployment. Your scope covers the full lifecycle: from evaluating a robot platform before it exists in hardware, through onboarding and integration, to production monitoring and maintenance.

Responsibilities• Define what "great developer experience" looks like for engineers working with humanoid robots, a category where the playbook is still being written. • Own the product roadmap for our developer tooling: SDKs, APIs, CLI tools, GUIs, diagnostics, and integration frameworks. • Own the productized path to policy deployment on robots at scale, including how to measure how the real hardware differs from its model, how to close that gap, which parts of the pipeline need to be self-served, and runtime observability tooling. • Support hardware design engagements with robot manufacturers, translating simulation-based capability analysis into design recommendations their mechanical teams can act on. • Get hands-on with robots. Run integrations yourself, reproduce bugs, and validate that tooling works in real-world conditions. • Establish documentation, tutorials, and reference architectures that let customer teams self-serve wherever possible, and monitor adoption with both quantitative signals and qualitative feedback to prioritise the backlog.