Senior Research Infrastructure Engineer

HUD•Singapore, CA

5d•$105,000 - $260,000

About The Position

HUD is building reliable, fair, and open infrastructure for AI data. The Senior Research Infrastructure Engineer will accelerate research on training and evaluating frontier AI agents by building the software and infrastructure researchers use to iterate on environments, collect trajectories, and evaluate model behavior. The role works closely with research engineers to understand their questions, identify experimentation bottlenecks, and turn research code into reliable, reusable systems. The engineer owns tools, pipelines, and execution infrastructure that help researchers explore ideas, reproduce results, and scale promising approaches. The position is full-time. HUD has offices in San Francisco and Singapore and is open to remote candidates able to work hours with 70–80% overlap with either location’s time zone. HUD supports relocation and visas for strong full-time candidates moving to the US or Singapore. Applications are rolling, and the hiring process consists of two technical interviews and a 2–3 day work trial.

Requirements

Strong software engineering fundamentals and proficiency in Python, with a record of building maintainable systems used by others.
Experience owning backend services, data pipelines, developer tools, or infrastructure from design through deployment and operation.
Sound judgment about APIs, data models, asynchronous workloads, and tradeoffs in building reliable systems.
Comfort with Docker, Linux, cloud infrastructure, and debugging failures across application code and the systems it runs on.
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 users and workloads grow.

Nice To Haves

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.

Responsibilities

Build infrastructure for agent behavior research, including reliable environment execution, trajectory collection, and evaluation at scale.
Develop reusable tools, APIs, and pipelines for launching experiments, managing datasets and configurations, and inspecting 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.
Improve orchestration, observability, and recovery from failed runs to make experimentation faster, more reproducible, and more cost-efficient.
Partner with research and engineering teams to translate evolving research needs into infrastructure priorities and architecture decisions.
Own systems from implementation through ongoing operation.

Benefits

Competitive compensation.
100% covered top-of-the-line medical, dental, and vision for US and Singapore employees.
Lunch and dinner for in-office employees.
Company-wide holiday break from Christmas Eve to New Year’s Day, in addition to PTO and paid holidays.
401(k) with a 4% match.
Equinox membership and commuter benefits for US employees.
Health and wellness stipend for Singapore employees.
Unlimited access to tokens for ChatGPT, Claude Code, Cursor, and similar tools.
Annual travel budget to visit either office.
Relocation and visa support for strong full-time candidates moving to the US or Singapore.