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 Research Manager to lead research that makes HUD’s agent training data and evals more useful for improving frontier models. You’ll lead research engineers through ambiguous projects, and turn findings into methods that can be used at scale.

You’ll stay close to the technical work while helping the team choose the right questions, run rigorous experiments, and deliver results. This role calls for an understanding of how and why models train: which signals teach useful behavior, where apparent progress is misleading, and how data and eval design can change outcomes.

Responsibilities

Set the research direction for data quality, including how HUD measures how training data impacts agent behavior and performance after training, and what makes data valuable

Lead synthetic data efforts to understand how various data types can be efficiently transformed into formats useful for training

Lead research engineers from problem definition through experiments, implementation, and clear conclusions; coach them to strengthen technical judgment and execution

Design and review experiments that connect model behavior and failure modes to data, environment, and reward design

Develop methods for validating and improving training data at scale, including trajectory audits, adversarial checks, and feedback loops that help users improve their data

Partner with research engineers, domain experts, and data vendors to turn research insights into better workflows, tools, and quality standards

Communicate findings and tradeoffs clearly so the team can prioritize work and apply what it learns across research areas

Experience

You may be a good fit if you have

Experience leading technical research projects to completion, from an open question to evidence, a decision, and a working result

Experience directly managing and mentoring researchers or research engineers while remaining engaged in technical work

A strong understanding of machine learning and reinforcement learning, including how training objectives, data, and feedback shape model behavior

Experience with agent training data, evals, benchmarks, synthetic data, or model evaluation infrastructure

Sound experimental judgment: you can distinguish a useful training signal from a task or metric that only looks convincing

Strong written communication and the ability to explain methods and findings to researchers, engineers, and external partners

Strong candidates may also have

Built scalable data quality systems or validation pipelines for model training

Experience diagnosing reward hacking, grader errors, or other subtle agent failure modes

Experience translating research findings into tools and processes used by others

Early-stage startup experience and strong communication skills for collaboration across teams and time zones

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.