San Francisco / Singapore / Remote (Asia) / Remote (North America)
Location
In office
Work mode
Full-time
Schedule
Senior
Level
$175K–$260K
Compensation
Job description
ABOUT HUD
HUD hud.ai'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 Senior Research Infrastructure Engineer to accelerate research on training and evaluating frontier AI agents. You’ll build the software and infrastructure that enable HUD’s researchers to iterate on environments, collect trajectories, and evaluate model behavior.
You’ll work closely with research engineers to understand the questions they’re investigating, identify bottlenecks in experimentation, and turn research code into reliable, reusable systems. You’ll own the tools, pipelines, and execution infrastructure that help researchers explore ideas, reproduce results, and scale promising approaches.
RESPONSIBILITIES
- Build infrastructure that supports research on agent behavior through reliable environment execution, trajectory collection, and evaluation at scale
- Develop reusable tools, APIs, and pipelines that let researchers launch experiments, manage datasets and configurations, and inspect trajectories and evaluation results
- Turn research prototypes and experimental workflows into tested, documented software that researchers can reuse and extend
- Design experiments to understand model behavior, agent failure modes, and data quality issues
- Make experimentation faster, more reproducible, and more cost-efficient by improving orchestration, observability, and recovery from failed runs
- Partner with research and engineering to translate evolving research needs into infrastructure priorities and architecture decisions, owning systems from implementation through ongoing operation
EXPERIENCE
You may be a good fit if you have:
- Strong software engineering fundamentals and proficiency in Python, with a record of building maintainable systems used by others
- Ownership of backend services, data pipelines, developer tools, or infrastructure from design through deployment and operation
- Sound judgment about APIs, data models, asynchronous workloads, and the tradeoffs involved in building reliable systems
- Comfort with Docker, Linux, cloud infrastructure, and debugging failures across application code and the systems it runs on
- The ability to work closely with researchers, understand experimental workflows, and translate open-ended research needs into useful software
- Independent technical judgment to set priorities, make pragmatic architecture decisions, and improve systems as their users and workloads grow
Strong candidates may also have:
- Working knowledge of reinforcement learning, including environments, rollouts, reward signals, and evaluation workflows
- Experience building research infrastructure, ML pipelines, or tools that made a research team faster
- Familiarity with distributed job execution, containerized environments, or high-concurrency workloads
- A track record of improving experiment reproducibility, data lineage, or visibility into complex pipeline failures
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), the former #1 ranked Catan player in the world, 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
- 401k with a 4% match
- Other perks including an Equinox membership 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): $175,000 - $260,000
- Singapore: $130,000 - $195,000
- Rest of world: $105,000 - $195,000
Due to high volume, we may not actively respond to every application, but feel free to contact us at recruiting@hud.so or elsewhere if we missed your application!
Requirements
Must-haves
Build infrastructure that supports research on agent behavior through reliable environment execution, trajectory collection, and evaluation at scale
Develop reusable tools, APIs, and pipelines that let researchers launch experiments, manage datasets and configurations, and inspect trajectories and evaluation results
Turn research prototypes and experimental workflows into tested, documented software that researchers can reuse and extend
Design experiments to understand model behavior, agent failure modes, and data quality issues
Make experimentation faster, more reproducible, and more cost-efficient by improving orchestration, observability, and recovery from failed runs
Partner with research and engineering to translate evolving research needs into infrastructure priorities and architecture decisions, owning systems from implementation through ongoing operation
You may be a good fit if you have:
Strong software engineering fundamentals and proficiency in Python, with a record of building maintainable systems used by others
Ownership of backend services, data pipelines, developer tools, or infrastructure from design through deployment and operation
Sound judgment about APIs, data models, asynchronous workloads, and the tradeoffs involved in building reliable systems
Comfort with Docker, Linux, cloud infrastructure, and debugging failures across application code and the systems it runs on
The ability to work closely with researchers, understand experimental workflows, and translate open-ended research needs into useful software
Independent technical judgment to set priorities, make pragmatic architecture decisions, and improve systems as their users and workloads grow
Working knowledge of reinforcement learning, including environments, rollouts, reward signals, and evaluation workflows
Experience building research infrastructure, ML pipelines, or tools that made a research team faster
Familiarity with distributed job execution, containerized environments, or high-concurrency workloads
A track record of improving experiment reproducibility, data lineage, or visibility into complex pipeline failures
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 Size: ~25 people currently, mostly full-time in-person, but some remote.
Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), the former #1 ranked Catan player in the world, 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.
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.
Nice-to-haves
None flagged.
Application prep
Use the role’s language and application details while tailoring your materials.