Synthetic Data Research Engineer: Scale Realistic Training

HUD; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Medical + dental + vision

Lunch and dinner in office

Holiday break

Equinox membership

401k

Commuter benefits

AI tokens access

Job summary

HUD is seeking Research Engineers to build our synthetic data pipeline. You’ll turn domain‑specific workflows into synthetic training tasks that are realistic enough to teach useful behavior, structured enough to generate at scale, and difficult enough to expand model capabilities.

You will work with subject‑matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains, design generation methods, and build tooling to mutate, validate, and

Qualifications

• Proficiency in Python, Docker, and Linux environments.
• Experience with synthetic data research methods – please elaborate in your application.
• Strong understanding of what “good synthetic data” means and its limitations.
• Built synthetic data pipelines end‑to‑end without a fully prescribed roadmap.

Responsibilities

• 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
• Analyze model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail
• Develop metrics to quantify and understand synthetic task diversity, realism, learnability, etc.

Skills

Python

Docker

Linux

Synthetic data

Job description

HUD is seeking Research Engineers to build our synthetic data pipeline. You’ll turn domain‑specific workflows into synthetic training tasks that are realistic enough to teach useful behavior, structured enough to generate at scale, and difficult enough to expand model capabilities.

You will work with subject‑matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains, design generation methods, and build tooling to mutate, validate, and