HUD

Research Engineer, Synthetic Data

San Francisco · Full-time

Sponsorship not specifiedDetected 8 days ago

PythonDockerLinuxData EngineeringAgentic AILogisticsResearchCommunicationCollaboration

About the role

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.

Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains
Design synthetic task generation methods that produce diverse, realistic, and learnable tasks
Build systems and tooling to mutate, validate, and improve synthetic tasks
Develop metrics to quantify and understand synthetic task diversity, realism, learnability, etc.
Be able to reason from first principles about task design, scoring, and failure modes

Proficiency in Python, Docker, and Linux environments
Have experience with synthetic data research methods - please elaborate in your application
Strong understanding of what "good synthetic data" means and its limitations
Experience working on environments, evals, and benchmarks
Early-stage startup experience with ability to work independently in fast-paced environments
You may be a good fit if you have:

100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)
Company-wide holiday break (Christmas Eve to New Year's Day) on top of PTO and paid holidays
Other perks including an Equinox membership, 401k, and commuter benefits (US employees)

We have 8 figures in funding and high revenue growth.
We're scaling profitably and quickly to meet very strong demand.
Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.

Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.

This listing is sourced directly from HUD's careers page and normalized into a canonical job model.