Humanity is in a virtuous cycle: human insight improves AI, and better AI expands what people can do. Sustaining it depends on the one input that can't be automated: expert human judgment.
At Pareto, we build the platform that turns that judgment into the data, evals, and RL environments frontier models learn from. We work with leading frontier labs like Anthropic and GDM, and we give skilled people everywhere a way to shape the future of AI and share in what it creates.
This RL environment and human-data infrastructure is already in production. Our job now is to scale it.
We're hiring a Head of Engineering to lead the teams behind our RL environment and human-data platform. You'd report to our founder and CEO, Phoebe Yao, and work closely with Research, Applied AI, and Operations.
The team is already strong with two team leads. Your job is to make them better and speed up what they ship for the labs at the frontier. You won't write much code here, but you can't be a stranger to it. When an incident hits, we want you in the room, and you should be able to tell an engineer, honestly, when their work isn't ready. We'll also want your read on where RL post-training goes next.
Leadership and development. Lead, coach, and develop the engineering team. Set the performance bar, build a healthy culture, and grow the leads beneath you into the next layer of leadership.
Hiring and capacity. Own hiring and headcount in a highly competitive talent market. Be honest about what the team can actually take on.
Technical direction. Set the quality bar for our RL environment and human-data infrastructure, and make the architecture calls that let it scale. The hard part is keeping the training signal clean as volume climbs.
Cross-functional partnership. Be the engineering point of contact for Research, Applied AI, and Operations, and keep the roadmap honest about what each of them needs from you.
AI-native execution. Coding agents should be the default way work gets done on your team, not a science experiment.
You've managed at real scale. You've run an engineering team of meaningful size, managers or strong tech leads included, and grown both the people and the systems through fast growth.
You're still technical. You can read a PR and push back on an architecture decision. In an incident you're useful, not a bystander, even if it's been a while since you shipped commits.
People stay for you. You've grown managers and held onto good engineers in a market that keeps trying to take them.
You know this field. You can hold a real conversation about LLMs, agents, evals, and RL environments, and you've got a view on where post-training goes next. You don't have to be a researcher, but you do have to be credible.
You're in SF. Hybrid, at least two days a week in the office.
Want long planning cycles and tidy quarterly roadmaps. We move in weeks, and the plan shifts as the frontier does.
Want clean handoffs between strategy, architecture, and execution. Here that's all one job, and it's yours.
Think "AI-native" is a slide, not a way of working.
Will trade the quality bar for speed. Bad training signal is worse than slow, every time.
Base salary $260,000 to $310,000, plus equity. Final offer depends on experience and level.
A lot of the human signal behind frontier models runs through Pareto, and you'd own the engineering behind it. The seat gets bigger as the company does.
Apply even if you don't match every line above. We care more about how you think and lead than about a clean résumé.
Compensation Range: $260K - $310K
