
Member of Technical Staff - Research / Post-Training
Job Description
About Halluminate
Halluminate is an applied AI and data company building computer-use and tool-use RL gyms to train AI agents on real financial services work — Excel modeling, pitch decks, and everything investment banking, private equity, and hedge funds rely on. We work with frontier labs, graduated from Y Combinator (S25), and raised a seed round to scale the team.
The role
Base: $200–275k · Equity: 0.15–0.30% · Location: San Francisco, in-person
Lead our in-house research and post-training efforts.
Responsibilities
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Train open-source frontier models on our in-house environments to validate quality and build research insights.
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Lead our public-facing benchmark and leaderboard efforts for frontier models.
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Publish findings in blogs and papers to share with the broader research community.
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Contribute to core platform engineering as needed — AI tooling, data collection pipelines, partnering with environment and reward designers.
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Build our engineering team culture from the ground up.
You should have
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Experience in model post-training.
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Experience publishing papers and sharing open research.
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Familiarity building evaluations, benchmarks, or RL for AI agents.
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Startup speed: iterate quick, ask quick, respond quick.
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Strong product and user ownership.
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Client-facing comfort — you'll talk to users, customers, and SMEs.
Nice to have
- Former founders or experience at early-stage startups.
What we offer
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Healthcare — Fully covered medical, dental, and vision for you. Subsidized for dependents.
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Relocation — $10k+ relocation stipend to help you get to San Francisco.
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401(k) — Up to 4% employer match.
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Food — Free breakfast, lunch, and dinner while working.
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Health — Free gym membership.
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Transportation — Free Ubers and Waymo rides to and from the office.
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Visa sponsorship — We sponsor visas, including H-1B.
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Job Details
- Category
- Research
- Employment Type
- Full Time
- Location
- San Francisco, CA
- Posted
- Compensation
- $200,000 - $275,000 per year
About Halluminate
Data and RL environments to automate knowledge work
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