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resolution Verified 7d ago

Lean Engineer

Berkeley, California, United States Hybrid

Salary not listed
PaySalary not listed
TypeFull-time
Work settingHybrid
Verified listing

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About the role

Many teams at Resolution make use of Lean to accelerate their research, including Learning Theory (see our recent autoformalization of Hironaka’s Resolution of Singularities ), Scalable Oversight (which is aiming for things like formal guarantees and impossibility results over debate protocols), and Agent Foundations (which is formalizing results on feedback, fixed points, and previsions in Lean). Although these projects operate independently, they share a need for knowledge, infrastructure, tools, and wisdom for doing theory research with Lean. You would be the first person to drive this initiative. This means working closely with researchers and engineers in our research divisions, as well as with engineers in central engineering and automation roles. You would be responsible for making Resolution good at Lean at scale. At the highest level, this means providing taste and expertise in Lean and autoformalization. Resolution already houses several Lean users, but none of us are experts. So far we’ve gotten by on using AI agents to write Lean for us and on our general mathematics and software-engineering judgment. But this is not enough as our Lean efforts continue to grow. We are looking for someone who can guide us in our Lean usage and think strategically about what structures to build to make the various org autoformalization efforts go better. This will let us accelerate our theoretical research efforts, especially as we hand off more and more research to AI agents. Importantly, this role does not mean you will be writing Lean. Agents will continue to write almost all of our Lean and will be the primary consumers of the Lean we produce. The volume of Lean will be larger than any human can individually interpret. Your job is to make this reliable and effective at that scale: set the conventions, architecture, and review structures that let researchers set the right definitions and trust agent output without reading every proof.

What you'll bring

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