Member of Technical Staff - Research / Post-Training
About the role
Lead our in-house research and post-training efforts at Halluminate, a data research lab building benchmarks and RL environments to advance AI in knowledge work. Focused on training frontier models, publishing findings, and developing the core platform with a hands-on, startup-fast approach in San Francisco.
Responsibilities
- Train open-source frontier models on our in-house environments to validate quality and build research insights.
- Lead our public-facing benchmark and leaderboard efforts for frontier models.
- Publish findings in blogs and papers to share with the broader research community.
- Contribute to core platform engineering as needed — AI tooling, data collection pipelines, partnering with environment and reward designers.
- Build our engineering team culture from the ground up.
Qualifications
- Experience in model post-training.
- Experience publishing papers and sharing open research.
- Familiarity building evaluations, benchmarks, or RL for AI agents.
- Startup speed: iterate quick, ask quick, respond quick.
- Strong product and user ownership.
- Client-facing comfort — you'll talk to users, customers, and SMEs.
Skills
Benefits
- Healthcare — Fully covered medical, dental, and vision for you. Subsidized for dependents.
- Relocation — $10k+ relocation stipend to help you get to San Francisco.
- 401(k) — Up to 4% employer match.
- Food — Free breakfast, lunch, and dinner while working.
- Health — Free gym membership.
- Transportation — Free Ubers and Waymo rides to and from the office.
- Visa sponsorship — We sponsor visas, including H-1B.
About Halluminate
Data and RL environments to automate knowledge work
We help foundation model labs and enterprises train computer use AI with data and sandboxes. A knowledge worker spends 90% of their day on a computer. A productive AI agent must similarly learn how to use our computers, browsers, and software to deliver real world value. Today, browser and computer use AI is inaccurate, slow, and expensive. Model labs and enterprises who want to improve these agents are bottlenecked by two resources: 1) High quality datasets for benchmarking and evaluations 2) Realistic sandbox environments for safe and accurate testing/training (ex. a simulated version of Salesforce) Halluminate offers a suite of products and services to address both these issues. Our evaluation service combines proprietary datasets with high quality annotations to help our customers identify and prioritize the biggest failure modes of their computer- and browser- use AI. Our platform provides a catalog of fully managed sandboxes, empowering our customers to safely and accurately test/train at scale, resulting in improved performance. Our customers see - Improved browser- and computer-use agent performance - New and emergent frontier agent capabilities - Data driven prioritization leading to exponentially faster development speed - Increased revenue/sales via marketing from public benchmarks Our paying customers already include leading computer use model labs and the two largest browser agent companies in the space. Wyatt and Jerry are friends that met their first week of school studying CS at Cornell University. They’ve been working and studying together for 7+ years.
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