Full-Stack RL Engineer: Build Data Engine & Dashboards

hud; Singapore; Hybrid; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Medical/dental/vision coverage (US)

Lunch and dinner in office

Equinox membership

401k

Job summary

HUD is seeking a Full-Stack Software Engineer to build product surfaces, backend systems, and internal tools powering the RL data engine. You will own end-to-end experiences—from frontend dashboards to backend services enabling frontier labs and external partners to create, evaluate, and iterate on reinforcement learning training data.

You'll collaborate with research engineers and vendors to translate complex needs into polished products, delivering scalable systems and user-friendly interfaces

Qualifications

• Proficiency in Python and a modern web stack (React, TypeScript, Next.js).
• Experience shipping user-facing or internal products end-to-end.
• Ability to design intuitive tools for both technical and non-technical users.
• Comfort with cloud infrastructure and production debugging.
• Strong communication across research, engineering, operations, vendors, and founders.

Responsibilities

• Develop product-facing tools for browsing environments, inspecting trajectories, and reviewing task quality.
• Build vendor-facing workflows for external partners to create and test RL environments and data.
• Create dashboards and observability tools for environment quality, eval results, and data collection progress.
• Design backend services and APIs connecting authoring, data collection, evaluation, QA/QC, and RL training infra.
• Partner with research, operations, and GTM teams to ship well-designed systems quickly.

Skills

Full-stack fundamentals

Python

React/TypeScript/Next.js

End-to-end product ownership

Cloud infrastructure

Docker

CI/CD

Observability

Communication

Data tooling familiarity

Tools

Docker

CI/CD

Observability tools

Job description

HUD is seeking a Full-Stack Software Engineer to build product surfaces, backend systems, and internal tools powering the RL data engine. You will own end-to-end experiences—from frontend dashboards to backend services enabling frontier labs and external partners to create, evaluate, and iterate on reinforcement learning training data.

You'll collaborate with research engineers and vendors to translate complex needs into polished products, delivering scalable systems and user-friendly interfaces