Applied AI Research Engineer - Build Tools for Frontier

HUD; Singapore; On-site; SGD 232,437 - 309,916; Full time

Benefits offered by this job

Competitive compensation

Top-tier medical, dental & vision

Lunch & dinner in office

Company-wide holiday break

Equinox membership

401k

Commuter benefits

Unlimited access to AI tokens

Job summary

HUD is seeking an Applied Research Engineer to own implementation and debugging across RL training data pipelines. You will diagnose ambiguous problems and lead deployment workflows for frontier AI labs, data vendors, and internal teams.

This role emphasizes fast yet correct delivery and hands-on tool-building to accelerate customer success. Ideal candidates combine strong Python, Docker and Linux skills with experience in benchmarks/evals and the ability to operate independently in ambiguous

Qualifications

• Proficiency in Python, Docker, and Linux environments.
• Experience with benchmarks and evals enabling realistic task evaluation.
• Strong debugging across code, data, and environments.
• Ability to work independently in ambiguous situations.
• Good judgment on speed vs. correctness, including security considerations.

Responsibilities

• Own deployment requests from labs, vendors, and internal teams from triage to completion.
• Clarify ambiguous asks and identify what actually needs to be done.
• Build tools and pipelines to solve urgent customer problems.
• Coordinate with research and GTM teams to unblock deployments.
• Balance speed and quality to meet fast-turnaround needs.
• Document recurring issues and turn manual work into reusable tools.

Skills

Python

Docker

Linux

Benchmarks/Evals

Debugging

Independent work

Customer-facing

Tools

Git

Job description

HUD is seeking an Applied Research Engineer to own implementation and debugging across RL training data pipelines. You will diagnose ambiguous problems and lead deployment workflows for frontier AI labs, data vendors, and internal teams.

This role emphasizes fast yet correct delivery and hands-on tool-building to accelerate customer success. Ideal candidates combine strong Python, Docker and Linux skills with experience in benchmarks/evals and the ability to operate independently in ambiguous