Research Engineer (General)

About the role

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

This is a general application for candidates who are unsure which research focus - QC Automation, Benchmarks, or Synthetic Data - they would be a fit for. We would love to meet you and figure it out together. However, if you already have a focus in mind, please apply to only that application.

We're looking for Research Engineers to build the technical foundation for training and evaluating frontier AI agents. You’ll build the systems for creating new environments, improve data quality, and translate real-world workflows into tasks and benchmarks.

Responsibilities

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Build systems for creating, running, evaluating, and improving agent training environments

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Design experiments to understand model behavior, agent failure modes, and data quality issues

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Develop tools that help researchers, engineers, and data vendors create higher-quality tasks, trajectories, and feedback loops

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Work across the full lifecycle of agent training data - task design, environment setup, trajectory collection, evaluation, and validation

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Partner with external vendors to identify bottlenecks and improve the quality and throughput of HUD’s data engine

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Build metrics and analyses that help us understand whether our tasks, environments, and evals are actually useful for training frontier agents

Experience

You may be a good fit if you have:

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Proficiency in Python, Docker, and Linux environments

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Experience working on benchmarks and evals - you can reason about what makes a task realistic, a rubric reliable, an environment usable, and a trajectory useful for RL training

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Strong attention to detail and the ability to spot subtle inconsistencies in data, model behavior, or task design

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Experience building tools, pipelines, experiments, or infrastructure without a fully prescribed roadmap

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Early-stage startup experience with ability to work independently in fast-paced environments

Strong candidates may also have:

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Experience building internal tools, research infrastructure, or data pipelines

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Experience designing metrics and validation workflows

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A background in competitive programming, Olympiad medaling, research, or unusually strong independent project experience