About the role
About HUD
HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.
About the role
We’re looking for Research Engineers to build our synthetic data pipeline. You’ll turn domain-specific workflows into synthetic training tasks that are realistic enough to enough to teach useful behavior, structured enough to generate at scale, and difficult enough to expand model capabilities.
Responsibilities
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Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains
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Design synthetic task generation methods that produce diverse, realistic, and learnable tasks
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Build systems and tooling to mutate, validate, and improve synthetic tasks
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Analyze model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail
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Develop metrics to quantify and understand synthetic task diversity, realism, learnability, etc.
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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Have experience with synthetic data research methods - please elaborate in your application
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Strong understanding of what “good synthetic data” means and its limitations
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Built synthetic data pipelines end-to-end without a fully prescribed roadmap
