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Senior AI Research Engineer (Agentic AI Systems)

Stealth Startup โ€ข San Francisco Bay Area
Visa Sponsorship Relocation
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AI Summary

Build and advance an AI system capable of original discovery, bridging research and engineering across data, training, evaluation, and deployment. Own end-to-end loops in cutting-edge AI architectures, with emphasis on hands-on execution and rapid learning. Join a stealth AI lab in the San Francisco Bay Area with competitive compensation and growth opportunities.

Key Highlights
Build an AI system capable of original discovery, not just retrieval or recombination of existing knowledge
End-to-end ownership of data, training, evaluation, and deployment loops with agentic architectures
Hybrid role requiring strong machine-learning intuition, engineering fundamentals, and rapid learning of new technical layers
Key Responsibilities
Develop and refine AI architectures capable of original discovery and reasoning beyond existing knowledge
Own full data, training, evaluation, and deployment pipelines for AI systems
Collaborate across research and engineering to build and iterate on agentic AI systems
Technical Skills Required
Machine Learning Large Language Models (LLMs) Python
Benefits & Perks
Full-time salaried position
Hybrid work in the San Francisco Bay Area
Relocation support and visa sponsorship where needed

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'


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.


The terms are plain: a full-time salaried appointment, hybrid in the San Francisco Bay Area, with relocation support and visa sponsorship where needed. base salary plus meaningful early-stage equity, and compute to match the ambition. Medical, dental, vision, 401(k).


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.


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