Build an AI capable of original discovery through end-to-end system development. Design and implement post-training, agentic architectures, and underlying infrastructure. Demonstrate rapid learning and reproducible engineering across the full ML stack.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
We are a frontier artificial-intelligence laboratory, still in stealth, established for a single purpose: to build an AI capable of original discovery โ a system that does not merely retrieve and recombine what is already known, but reasons toward genuinely new understanding of the problems put to it. We hold that an instrument able to truly discover is among the most consequential technologies humanity will ever build, and we are building it.
Our system has already produced results beyond the reach of any human team.
'Others in this space have celebrities and slides, we have actual results'
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It is one machine โ post-training, agentic architectures, the data and evaluation layers beneath them โ and we hire people who can build any part of it. The work is whatever the instrument needs next.
We do not separate research from engineering, and we do not carve the system into territories. You run whole loops โ data, training, evaluation, analysis โ and you ship.
The trait we weight above every other is not what you already know, but how fast you learn what no one can teach you. What we look for is range and proof: strong machine-learning intuition and serious engineering fundamentals; hands-on experience with LLM systems โ post-training, agents, or the infrastructure beneath them; the ability to learn a new layer of the stack in weeks, not quarters; and code others can reproduce. We weigh demonstrated ability above pedigree.
The terms are plain: a full-time salaried appointment, hybrid in the San Francisco Bay Area, with relocation support and visa sponsorship where needed. $320,000 base salary plus meaningful early-stage equity, and compute to match the ambition. Medical, dental, vision, 401(k).
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What we look for is range and proof: strong machine-learning intuition and serious engineering fundamentals; hands-on experience with LLM systems โ post-training, agents, or the infrastructure beneath them; the ability to learn a new layer of the stack in weeks, not quarters; and code others can reproduce. We weigh demonstrated ability above pedigree.
Apply here:
https://pfa.zto77.com/apply-technical-staff
Every application receives an answer within days.
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