Full-Stack Software Engineer, Reinforcement Learning

Human Union Data, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical benefits

Lunch and dinner in office

Holiday break and PTO

Equinox membership

401k

Commuter benefits

Unlimited access to AI tokens

Job summary

Human Union Data, Inc. is seeking a Full-Stack Software Engineer, Reinforcement Learning to build product surfaces, backend systems, and internal tools powering our RL data engine.

You’ll own end-to-end development of backend services, APIs, databases, dashboards, and vendor workflows for RL environments and data collection. You’ll collaborate with research, operations, and GTM teams to translate vague needs into polished products that ship quickly, with a focus on Python, React/TypeScript, and

Qualifications

• Strong software engineering fundamentals and full-stack range.
• Experience owning user-facing or internal products end-to-end.
• Ability to translate vague needs into polished products.
• Comfort with cloud, Docker, CI/CD, observability, and production debugging.
• Strong communication across research, engineering, operations, vendors and founders.

Responsibilities

• Develop product-facing tools for environments, trajectories, and model behavior.
• Build vendor workflows for RL environments and data submission.
• Create dashboards and observability tools for data and RL pipeline health.
• Design backend services and APIs linking authoring, data collection, evaluation, and training.
• Collaborate with research, operations, and GTM to ship well-designed systems quickly.

Skills

Python

React

TypeScript

Next.js

Full-stack

Cloud infrastructure

Docker

CI/CD

Observability

Communication

Ownership

Tools

AWS

Kubernetes

Grafana

Job description

About HUD

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.

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.

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: ~15 people currently, mostly full-time in-person, but some remote.

• Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.

• Company stage: We have 8 figures in funding and high revenue growth. We’re scaling profitably and quickly to meet very strong demand.

Logistics

• Employment: Full-time.

• Location: On-site only, for now. You can join the team in the San Francisco Bay Area or Singapore offices.

• 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 interviews and a 2-3 day work trial.

What we offer

• Competitive compensation

• 100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)

• Lunch and dinner when you’re in the office

• Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays

• Other perks including an Equinox membership, 401k, and commuter benefits (US employees)

• Unlimited access to tokens for ChatGPT, Claude Code, Cursor, etc. By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.

Due to high volume, we may not actively respond to every application, but feel free to contact us at [e-mail] or elsewhere if we missed your application!