Full-Stack Software Engineer

Full-Stack Software Engineer, Reinforcement Learning

Compensation: $100K–$200K
Location: San Francisco, CA or Singapore — On-site
Visa: Relocation and visa sponsorship available for exceptional candidates
Employment: Full-time

About the Role

Build the product surfaces, backend systems, and internal tooling that power HUD’s reinforcement learning (RL) data engine.

As a Full-Stack Software Engineer, you’ll own critical product surfaces end-to-end—from backend services, APIs, and databases to dashboards, vendor workflows, data collection, and observability. You’ll work closely with research engineers and external vendors to turn ambiguous, technically complex needs into reliable products that ship quickly.

You don’t need to be an RL researcher. You do need strong engineering fundamentals, product instincts, and the ability to work across the stack and across teams.

What You’ll Do

• Build tools for browsing environments, inspecting trajectories, reviewing task quality, debugging failures, and understanding model behavior
• Develop vendor workflows for creating, submitting, testing, and iterating on RL environments and training data
• Build dashboards and observability systems for environment quality, evaluation results, data collection, grader issues, reward signals, and pipeline health
• Design backend services and APIs that connect task authoring, data collection, evaluation, QA/QC, and RL training infrastructure
• Own products from initial requirements through production, including architecture, implementation, deployment, and iteration
• Partner with research, operations, and GTM teams to turn ambiguous, high-stakes requests into systems that deliver measurable impact
• Improve the reliability, usability, and speed of the tools used to build and evaluate RL systems

What We’re Looking For

• Strong software engineering fundamentals and the ability to work comfortably across the full stack
• Proficiency in Python and a modern web stack such as React, TypeScript, or Next.js
• Experience owning user-facing products or internal platforms end-to-end
• Strong product judgment and an ability to build effective tools for both technical and non-technical users
• Experience with cloud infrastructure, Docker, CI/CD, observability, and production debugging
• High agency, strong ownership, and excellent cross-functional communication
• Comfort operating in ambiguous environments and rapidly turning ideas into production systems

Strong Signals

Experience building:

• Data collection, labeling, annotation, evaluation, or research tooling platforms
• Dashboards, review workflows, observability systems, or debugging interfaces for complex systems
• Developer tools, infrastructure products, or internal platforms that significantly improved team velocity
• Production systems using AWS, Kubernetes, Terraform, Docker, Grafana, or similar technologies

Most importantly, we’re looking for engineers who enjoy taking ownership, moving quickly, and building the systems that make ambitious technical teams dramatically more effective.