Software Engineer, Data Quality

Sorry, this job was removed at 10:24 p.m. (UTC) on Monday, Oct 19, 2026
18 Locations
In-Office or Remote
100K-225K Annually
Entry level
Artificial Intelligence • Information Technology • Software
The Role
Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.
Summary Generated by Built In
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 Software Engineer, Data Quality to build the tools that help HUD’s Quality and Intelligence (QNI) team assess and improve training data and evals for frontier agents. You’ll turn hands-on quality workflows into reliable internal products, then bring the most useful capabilities into HUD’s production platform.

You’ll work closely with QNI and research engineers across task review, trajectory inspection, grader checks, quality metrics, and feedback to data creators. The work spans interfaces, backend services, and data workflows, with a focus on making quality issues easier to find, understand, and fix.

Responsibilities
  • Build full-stack tools for reviewing tasks, inspecting agent trajectories, checking graders, and investigating data quality issues

  • Create dashboards and metrics that show quality trends, failure modes, and the health of review and validation workflows

  • Design backend services, APIs, and data workflows that connect quality checks with task creation, evaluation, and feedback to data creators

  • Work with QNI and research engineers to turn evolving review methods into clear, efficient workflows that scale beyond manual analysis

  • Move proven internal tools into production, improving their reliability, usability, and observability as adoption grows

  • Investigate issues in live workflows and use what you learn to improve the platform and prevent repeat failures

Experience

You may be a good fit if you have:

  • Strong software engineering fundamentals and the ability to build across frontend, backend, and data systems

  • Proficiency in Python and a modern web stack such as TypeScript and React, or comparable tools

  • Built internal or user-facing products end-to-end, from understanding a workflow through shipping and improving it

  • Sound judgment about APIs, data models, and production systems, including how to make them reliable and easy to debug

  • An ability to turn ambiguous quality problems into useful interfaces, automation, and measurable checks

  • Clear communication and comfort working closely with research and quality teams to understand how people use the tools you build

Strong candidates may also have:

  • Built tooling for data review, annotation, evals, benchmarks, or ML workflows

  • Worked with agent traces, graders, reward signals, or RL training data

  • Designed dashboards, observability tools, or review workflows for complex datasets and pipelines

  • Improved a prototype or internal tool until it was ready for wider production use

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), 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 interviews and a 2-3 day work trial.

What we offer
  • Competitive compensation

  • 100% covered top-of-the-line medical, dental, and vision (US and Singapore employees)

  • Lunch and dinner for in-office employees

  • 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) and a health/wellness stipend (Singapore 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.

  • Two top-floor offices in San Francisco’s Union Square and Singapore’s Raffles Place

  • Annual travel budget to visit either office

Compensation

Actual offers are adjusted for experience and location, but our base salary bands are

  • San Francisco (and other major US cities): $130,000 - $225,000

  • Singapore: $100,000 - $170,000

  • Rest of world: $100,000 - $170,000

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

Skills Required

  • Strong software engineering fundamentals across frontend, backend, and data systems
  • Proficiency in Python and a modern web stack such as TypeScript and React, or comparable tools
  • Experience building internal or user-facing products end-to-end
  • Sound judgment about APIs, data models, and production systems
  • Ability to turn ambiguous quality problems into interfaces, automation, and measurable checks
  • Clear communication and collaboration with research and quality teams
  • Experience building tooling for data review, annotation, evaluations, benchmarks, or ML workflows
  • Experience with agent traces, graders, reward signals, or reinforcement-learning training data
  • Experience designing dashboards, observability tools, or review workflows for complex datasets and pipelines
  • Experience improving prototypes or internal tools for wider production use

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The Company
HQ: Auckland
10 Employees
Year Founded: 2025

What We Do

The all-in-one platform for evaluations on computer use and browser use AI agents.

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