Backed by Y Combinator.
About the role
We’re looking for a Full-Stack Software Engineer, Reinforcement Learning to build the product surfaces, backend systems, and internal tools that power HUD’s RL data engine. You’ll own product surfaces end-to-end, including backend services, APIs, databases, dashboards, tools, vendor workflows, data collection, and observability for RL rollouts. You don’t need to be a researcher, but you need to work research engineers and vendors to translate ambiguous needs into polished products that enable our RL systems.
What they're looking for
- Strong software engineering fundamentals and real full-stack range, including proficiency in Python and a modern web stack such as React, TypeScript, Next.js, or similar
- Experience owning user-facing or internal products end-to-end
- Good product taste and the ability to build tools that are intuitive for both technical and non-technical users
- Comfort with cloud infrastructure, Docker, CI/CD, observability, and production debugging
- High agency—you identify what needs to exist, build it, and improve it without waiting for a perfect spec
- Strong communication skills for working across research, engineering, operations, vendors, and founders
More about this role
HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.
We’re looking for a Full-Stack Software Engineer, Reinforcement Learning to build the product surfaces, backend systems, and internal tools that power HUD’s RL data engine.
You’ll own product surfaces end-to-end, including backend services, APIs, databases, dashboards, tools, vendor workflows, data collection, and observability for RL rollouts. You don’t need to be a researcher, but you need to work research engineers and vendors to translate ambiguous needs into polished products that enable our RL systems.
Develop product-facing tools for browsing environments, inspecting trajectories, reviewing task quality, debugging failures, and understanding model behavior
Build vendor-facing workflows that make it easy for external partners to create, submit, test, and iterate on RL environments and training data
Create dashboards and observability tools that surface environment quality, eval...
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