Job description

THINK DIFFERENT. BUILD THE FUTURE. 🚀 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. (Watch the demo drive.google.com) If you see possibility where others see limits, read on. You decide what "better" means. Models, agents, and product features all ship behind one question: did this actually get better? Without a strong evals function, the lab ships vibes. With one, every training run, every prompt change, every agent capability moves a number we trust — and the team makes decisions on real signal, not the loudest opinion in the room. You'll build the eval harness for AGI — across model capability, agentic behavior, on-device performance, and end-user experience. You'll set the bar for what counts as "shipped" and protect it from the gravity of product deadlines. 🤩 TASKS YOU WILL OWN - The eval suites that gate every model and agent release — capability, behavior, regressions, and human-rated rubrics that catch what automated evals miss - The dashboards and tooling that make researcher experiment loops fast and leadership decisions easy - The bar — what counts as ready to ship, and how we know 🤚 AREAS WHERE YOU WILL ASSIST - Research, by making sure what we measure is what we want - Product engineers, by instrumenting real-user behavior on real devices - Partnerships, by translating "did it get better" into language an OEM partner can hold us to 📚 SKILLS YOU'LL BE EXPECTED TO TEACH - How to measure non-deterministic systems — agent eval, tool use, long-horizon tasks, multilingual behavior - How to push back on a metric that's being gamed without breaking the team 🧑‍🎓 SKILLS YOU'LL BE EXPECTED TO LEARN - On-device perf trade-offs and how they show up in real-user evals - What QA-ing AI at OEM scale actually looks like - The realities of shipping consumer agents to production partners 🏆 TIMELINE OF SUCCESS After 30 days — You've audited every eval we run today and produced a sharp doc on what's good, what's noise, and what's missing. You've fixed the most embarrassing gap. After 60 days — You've stood up a new eval surface — agentic, on-device, or behavioral — and the team is making real decisions on its output. Researchers come to you before launching a run, not after. After 90 days — Releases now ship against your eval bar, not a vibe-check. You've caught a regression that would have shipped, and cleared a launch the team was nervous about. You're shaping the research roadmap by surfacing where we're flat, where we're climbing, and where we're lying to ourselves. 💰 COMPENSATION Competitive cash and meaningful equity. Top-tier relocation and immigration support. SF, in person. HOW TO APPLY Send a link to an eval, benchmark, or measurement system you built — and one paragraph on what decision it changed. Plus your resume or LinkedIn. Every exceptional candidate hears back within 48 hours.

Requirements

Must-haves

The eval suites that gate every model and agent release — capability, behavior, regressions, and human-rated rubrics that catch what automated evals miss
The dashboards and tooling that make researcher experiment loops fast and leadership decisions easy
The bar — what counts as ready to ship, and how we know
Research, by making sure what we measure is what we want
Product engineers, by instrumenting real-user behavior on real devices
Partnerships, by translating "did it get better" into language an OEM partner can hold us to
How to measure non-deterministic systems — agent eval, tool use, long-horizon tasks, multilingual behavior
How to push back on a metric that's being gamed without breaking the team
On-device perf trade-offs and how they show up in real-user evals
What QA-ing AI at OEM scale actually looks like
The realities of shipping consumer agents to production partners
After 30 days — You've audited every eval we run today and produced a sharp doc on what's good, what's noise, and what's missing. You've fixed the most embarrassing gap.
After 60 days — You've stood up a new eval surface — agentic, on-device, or behavioral — and the team is making real decisions on its output. Researchers come to you before launching a run, not after.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: ashby
Effort: quick