AI Product Engineer

Our Mission Build everyday AGI. Trustworthy, consumer-grade agents that redefine human–AI collaboration for millions. Software shouldn’t wait for commands; it should partner with you, amplifying what you can do every single day. Why AGI, Inc. We’re a stealth team of elite founders and AI researchers, with backgrounds spanning Stanford, OpenAI, and DeepMind . We’re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale. Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts. We are supported by tier-1 investors who funded the first generation of AI giants; now they’re backing us to build the next everyday AGI. If you see possibility where others see limits, read on. The technical face of AGI inside the world's biggest device makers. You’ll be the connective tissue between AGI's research and engineering teams and the partners who put our agents in front of millions of users. You'll spend real time onsite — at global headquarters, R&D labs, and manufacturing facilities — turning vague partner asks into shipped capabilities. Then fly back, get into the codebase, and build the feature. This is for engineers who get bored shipping internal tools. You want your work in a keynote demo. Tasks you will own Path from “interesting idea on a partner call” to “feature shipped on a flagship device” — prompts, evals, mobile code, and the launch readiness doc Onsite technical leadership at one or more partners — you’re the senior eng they call when something matters End‑to‑end product surfaces for one or more agent capabilities, on‑device Areas where you will assist Research, by translating model improvements into partner wins Partnerships, by building partner‑ready demos that close deals and expand scope Product engineering, by bringing partner feedback back in a way that actually changes what we build Skills you’ll be expected to teach How to scope a partner ask without freezing — turning “make it magical” into prompt + model + UX decisions How to ship LLM‑powered features that survive contact with a real device, a real user, and a real launch date Skills you’ll be expected to learn On‑device inference, mobile constraints, and the engineering of running models outside the cloud World‑class consumer hardware product development, from the inside Reliable agentic systems from the people who published the canonical papers on it Timeline of success After 30 days — Onsite at least once with a partner. One agent capability shipped into our codebase. You know which model and agent assumptions break in their environment. After 60 days — Partner PMs ping you directly. A feature you shipped is running on partner devices in pre‑production. You can name, without a deck, the three things this partner needs from us next quarter — and you have prototypes for two of them. After 90 days — Your work is in a partner launch path. You’ve made the case for the next major bet on this account and the team has bought in. Partner leadership references you by name. Compensation Competitive cash and meaningful equity. Covered international travel for partner work. Top‑tier relocation and immigration support. SF, in person. #J-18808-Ljbffr