Delivery Engineer

About the role

ABOUT HUD

HUD https://www.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 Delivery Engineer to own complex, time-sensitive data and evaluation programs for frontier AI labs, data vendors, and internal teams. You’ll turn ambiguous technical asks into executable programs, coordinate the people and dependencies required to deliver them, and ensure the resulting data is genuinely useful for evaluating and improving LLMs and agents. You’ll also manage vendors and build the processes, tooling, and operating rhythms that allow HUD to deliver reliably at increasing scale.

RESPONSIBILITIES

- Own data and evaluation programs end-to-end, from initial scoping and requirements gathering through production, quality assurance, delivery, and retrospective

- Translate ambiguous requests from AI labs and internal teams into clear specifications such as milestones, owners, dependencies, acceptance criteria, etc.

- Maintain and improve data quality procedures using both quantitative signals and qualitative inspection, including analyzing task coverage, difficulty, reward distributions, etc.

- Maintain and improve delivery pipelines at scale, which includes managing external vendors throughout the delivery lifecycle

- Streamline the logistics behind data production and evaluation programs by improving workflows, documentation, automation, and cross-functional handoffs

- Partner with research, platform, marketplace teams to translate learnings and feedback into product improvements

EXPERIENCE

You may be a good fit if you have:

- Experience owning complex technical programs in AI/ML, data operations, support engineering, applied research, forward-deployed engineering, or a similarly cross-functional environment

- Strong quantitative and qualitative judgment about data—you can move between aggregate metrics and individual examples to determine whether a dataset or evaluation is useful, reliable, and representative

- Experience maintaining and improving complex processes, especially within project delivery or data contexts

- Demonstrated ability to bring structure to ambiguous problems, maintain momentum as requirements change, and make sound tradeoffs without waiting for a perfect specification

- Strong written and verbal communication skills, including the ability to turn messy technical information into clear decisions, owners, and next steps

Strong candidates may also:

- Experience with RLHF, reinforcement-learning environments, agent evaluations, human-in-the-loop data pipelines, annotation, or expert data collection