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 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 brings together five International Olympiad medalists across IOI, ILO and IPhO, researchers published at NeurIPS and ICLR, and engineers and researchers from Google DeepMind, Amazon, AWS, Meta, Qwen, GLM, and Mercor. It also includes operators from BCG and Uber, the first security hire at one of the largest crypto exchanges, serial AI startup founders, former quant researchers, artists and the former #1 ranked Catan player in the world.

Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand.