Job description

About Patronus AI Patronus AI is a frontier lab developing simulation research and infrastructure to accelerate progress toward human-aligned AGI. We are on a mission to simulate all of the world’s intelligence. We are the team behind some of the earliest and most influential research in AI evaluation like FinanceBench , Lynx , SimpleSafetyTests , CopyrightCatcher , Humanity’s Last Exam , and more. We are formerly AI researchers and engineers from companies like Meta AI, Amazon AGI, and Google. Our customers include foundation model labs and Fortune 500 enterprises like Adobe. We are backed by top-tier investors like Lightspeed Venture Partners, Notable Capital, Stanford University, Noam Brown, Gokul Rajaram, and more. Responsibilities We're building an exceptional team, and hiring is one of the most important things we do. As we scale, we're looking for a Technical Recruiter to own how we find, evaluate, and close the researchers and engineers who will do this work. This is not a role where you post a req and work an inbound queue. You'll own the end-to-end hiring process for research and engineering roles: building the pipeline, partnering with hiring managers and the co-founders on what the role actually is, and closing candidates who have other options. The people we hire are among the most heavily recruited in technology. Reaching them takes real fluency in what they work on — enough to read a paper's contribution, tell a strong open-source record from a busy one, and hold a credible conversation with an RL researcher and an infrastructure engineer in the same afternoon. Templates do not work on this market. Judgment does. The best person for this role has an opinion before the hiring manager does, sources the people who aren't looking, and holds the bar in a room full of founders who want the req filled. You use AI aggressively to take the mechanical work off your plate, and you spend the hours you win back on the calls only a person can make. In this role, you will: Manage full-cycle recruiting for technical roles across research, platform, product engineering and technical program management — intake through close. Source the passive market directly. Work from publications, citations, open-source contributions, conference participation, academic networks and referrals, not just keyword searches. Partner with hiring managers and the co-founders to turn a vague need into a real role: the scope, the bar, the loop, and what a yes actually looks like. Develop enough domain fluency to evaluate technical qualifications rather than match keywords, and to be a useful second opinion in a debrief. Close. Build the narrative for each candidate, handle competing offers, and run negotiations alongside the founders. Own candidate experience end to end. Every candidate, including the ones we pass on, should leave with a high opinion of us. Use AI aggressively. Automate market mapping, research, outreach drafting, and funnel analysis so more of your time goes to the judgment calls that cannot be automated. Report on pipeline health, time-to-fill, source effectiveness and offer outcomes, and use that data to change what we do. Build beyond the role. As you develop context and judgment, take ownership of increasingly ambiguous recruiting and People problems. Qualifications "The number one qualification to succeed in this machine learning course is gumption" - John Lafferty, CS Professor at Yale Above all, we look for a proactive mindset, willingness to learn, unlimited energy, and relentless optimism. You are a great fit if you have a background in the following: You have 5+ years of full-cycle technical recruiting, including time at a startup where the process did not already exist. You have hired engineers or researchers in a market where every good candidate had other offers. You can source. You can name the last five people you found who were not applying and not on the first page of results. You have the technical fluency to read a resume, a GitHub profile or a paper and form your own view before the hiring manager does. You close well. You can build a value proposition for a specific person, and you are comfortable in a negotiation. You have clear judgment about the bar, and you will hold it in a room with founders who want the req filled. You keep a clean ATS, report pipeline honestly, and can point at the evidence behind your decisions. You're excited about AI and constantly look for ways technology can make you more effective. You write exceptionally well. Your outreach is the first thing a candidate reads about us. Nice to Have: You've hired PhD-level researchers and know your way around arXiv, NeurIPS, ICML, ICLR and the people who publish there. You've recruited specifically for reinforcement learning, evaluation, agents or ML infrastructure. You've built your own tooling — scripts, automations, enrichment — rather than waiting for a vendor. You know Greenhouse well. You have a genuine interest in AI safety and evaluation with a realistic view of both the potential and the limits. To support close collaboration, this role is based in our San Francisco headquarters and requires in-office attendance 5 days a week. The expected base salary range for this role is $140,000 - $200,000 USD. In addition to base salary, we offer equity and benefits. Actual compensation will be determined based on experience, qualifications, skills, and location. Benefits Competitive salary and equity packages 15 days of paid vacation per annum Parental & sick leave Health, dental, and vision insurance plans 401(k) plan + matching In-office lunch & dinner Whoop band Monthly meal stipend Monthly health and wellness stipend Equinox membership Fun global offsites! Patronus AI is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. By clicking ‘Apply’, you agree to both of the following: 1) Greenhouse's Terms of Service and Privacy Policy 2) Patronus AI, Inc. Privacy Policy .

Requirements

Must-haves

Manage full-cycle recruiting for technical roles across research, platform, product engineering and technical program management — intake through close.
Source the passive market directly. Work from publications, citations, open-source contributions, conference participation, academic networks and referrals, not just keyword searches.
Partner with hiring managers and the co-founders to turn a vague need into a real role: the scope, the bar, the loop, and what a yes actually looks like.
Develop enough domain fluency to evaluate technical qualifications rather than match keywords, and to be a useful second opinion in a debrief.
Close. Build the narrative for each candidate, handle competing offers, and run negotiations alongside the founders.
Own candidate experience end to end. Every candidate, including the ones we pass on, should leave with a high opinion of us.
Use AI aggressively. Automate market mapping, research, outreach drafting, and funnel analysis so more of your time goes to the judgment calls that cannot be automated.
Report on pipeline health, time-to-fill, source effectiveness and offer outcomes, and use that data to change what we do.
Build beyond the role. As you develop context and judgment, take ownership of increasingly ambiguous recruiting and People problems.
"The number one qualification to succeed in this machine learning course is gumption" - John Lafferty, CS Professor at Yale
Above all, we look for a proactive mindset, willingness to learn, unlimited energy, and relentless optimism. You are a great fit if you have a background in the following:
You have 5+ years of full-cycle technical recruiting, including time at a startup where the process did not already exist.
You have hired engineers or researchers in a market where every good candidate had other offers.
You can source. You can name the last five people you found who were not applying and not on the first page of results.
You have the technical fluency to read a resume, a GitHub profile or a paper and form your own view before the hiring manager does.
You close well. You can build a value proposition for a specific person, and you are comfortable in a negotiation.
You have clear judgment about the bar, and you will hold it in a room with founders who want the req filled.
You keep a clean ATS, report pipeline honestly, and can point at the evidence behind your decisions.

Nice-to-haves

You've hired PhD-level researchers and know your way around arXiv, NeurIPS, ICML, ICLR and the people who publish there.
You've recruited specifically for reinforcement learning, evaluation, agents or ML infrastructure.
You've built your own tooling — scripts, automations, enrichment — rather than waiting for a vendor.
You know Greenhouse well.
You have a genuine interest in AI safety and evaluation with a realistic view of both the potential and the limits.
To support close collaboration, this role is based in our San Francisco headquarters and requires in-office attendance 5 days a week.

Application prep

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

Keywords to mirror

aimachine learningml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Wellness