Senior Software Engineer, RL Environments
- $245,000–$300,000 per year
- Hybrid
- All software engineering jobs
- FullTime
- Engineering
About the role
About Pareto
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.
The Role
You'll own the RL environments frontier labs train on, end to end. Scope the problem with the requester, build the image and the tools inside it, write the graders that score it, ship it into the customer's platform, and keep it healthy once it's running. You sit between Pareto's engineering team and the researchers at the labs we work with, close enough to both that you can tell when a training goal and a buildable spec have drifted apart.
Nobody will hand you a finished spec. You'll get a research problem, define what gets built, and stay with it after it lands. In your first year, good looks like environments that ship faster than the last one did, because you invested in the build and release path instead of hand-rolling each delivery. What you build becomes training signal. That's the reason the ownership runs all the way through production.
What You'll Own
The environment, end to end. Build industry-leading RL environments and MCP tools that power post-training loops for frontier labs.
Scoping with the requester. Partner closely with research labs, turn a rough training goal into a spec you can build against, and push back early when the ask won't produce usable signal.
Production health. Investigate failed tasks and jobs and take the lead when a delivery pipeline degrades.
Platform leverage. Automated image builds and release notes, spec-first design, CI gates, review harnesses. The next environment should cost a fraction of the last one.
The signal back into product. When the platform falls short of what a lab needs, you're the first to know. Getting that gap onto the roadmap is part of the job, not someone else's follow-up.
What We're Looking For
You’ve been building production systems for 7+ years. Enough range to tell which problems need a careful design and which just need shipping.
You write production Python or TypeScript. Strong functional-language background counts too. You'll be reading unfamiliar code and shipping changes to it in the same week.
You've built and shipped containerized services. Docker layering, dependency pinning, images that behave the same on the tenth run as the first. Reproducibility is the whole game here.
You get real leverage from coding agents, and you review what they produce. Fluent use is table stakes. The judgment to catch what the agent got wrong is the differentiator.
You own things after they ship. You've been on the hook for something in production, worked an incident to root cause, and written the doc that kept it from happening twice.
You know your way around cloud infrastructure. Containers, managed databases, and the deploy path, on AWS or any major cloud. You don't need to be an infrastructure specialist, but you do need to debug your own deploys.
You're based in the US and can get to the Bay Area as needed.
You Probably Aren't the Right Fit If You
Need requirements locked before you start. Specs here evolve with the research, and that's expected.
Prefer working at arm's length from stakeholders. This role is high-contact by design.
Want to hand off at launch. This role owns environments in production, including the bad weeks.
Only want to build one kind of thing. This role moves between image builds, grader design, and client work in the same week.
Compensation
Base salary $245K–$300K, plus equity. Final offer depends on experience and level, and we share level-specific ranges early in the process.
Why Pareto
The environments you build become the training signal for models at Anthropic and GDM. Not adjacent to that work. Inside it. Equity is part of the package at every level.
Apply even if you don't match every line above. We care more about how you think than we do about a clean résumé.
