Full-Stack Software Engineer, Reinforcement Learning at Hud in San Francisco, CA

Job description

About HUD HUD's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs. About the role We’re looking for a Senior 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. Responsibilities • 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 results, data collection progress, grader issues, reward signal problems, and pipeline health • Design backend services and APIs that connect task authoring, data collection, evaluation, QA/QC, and RL training infrastructure • Partner closely with research, operations, and GTM teams to turn vague, high-stakes requests into well-designed systems that ship quickly Experience You may be a good fit if you have: • 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 Strong candidates may also have: • Experience building data collection, labeling, annotation, eval, or research tooling platforms • Experience building dashboards, review workflows, observability tools, or debugging interfaces for complex systems • Experience building developer tools, infrastructure products, internal platforms, or workflow products that made a team dramatically faster • Experience with AWS, Kubernetes, Terraform, Docker, Grafana, or similar infrastructure tools as tools to ship product, not as the center of the role We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria. Team & company details • Team Size : ~25 people currently, mostly full-time in-person, but some remote. • Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), the former #1 ranked Catan player in the world, serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc. • Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand. Logistics • Employment : Full-time. • Location : We have offices in San Francisco or Singapore but are open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones. • Visa Sponsorship : We provide support for relocation and visas for strong full-time candidates to the US or Singapore. • Timeline : Applications are rolling. The process is 2 technical in