Lead Data Quality Engineer for RL Training Data (Remote)

HUD; Singapore; Hybrid; SGD 192,000 - 294,000; Full time

Benefits offered by this job

Competitive compensation

100% covered medical, dental, vision (

Lunch and dinner when in office

Holiday break plus PTO and holidays

Equinox membership, 401k, commuter ben

Unlimited tokens for AI tools

Job summary

HUD is building infrastructure to create RL training data and evals for frontier AI agents, with a marketplace to sell them to frontier labs. We seek a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents.

You will lead the data quality team across RL environments, synthetic data, benchmarks, and domain-specific workflows, mentoring engineers and shaping production tools and dashboards.

Qualifications

• Advanced proficiency in Python, Docker, and Linux environments.
• Strong data quality intuition and ability to reason about task realism, learnability, diversity, and usefulness for training.
• Experience building QC systems, evals, benchmarks, synthetic data pipelines, and validation workflows.
• Comfort working across messy human and technical systems with domain experts, vendors, and data generators.
• Clear written communication and ability to explain methodology to researchers, engineers, labs, and external audiences.

Responsibilities

• Lead HUD's data quality strategy, build QC systems, define quality standards, and design experiments to evaluate agent outputs.
• Develop methods for validating synthetic data at scale, including failure-mode analysis and trajectory auditing.
• Partner with researchers, domain experts, and vendors to diagnose quality issues and improve data generation workflows.
• Translate qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops.
• Help create internal research taste around what makes agent training data truly useful, not just superficially correct.
• Mentor other engineers to maintain high technical rigor, clarity, and execution speed.

Skills

Python

Docker

Linux

Data quality intuition

QC systems

Data pipelines

Communication

Tools

Docker

Job description

HUD is building infrastructure to create RL training data and evals for frontier AI agents, with a marketplace to sell them to frontier labs. We seek a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents.

You will lead the data quality team across RL environments, synthetic data, benchmarks, and domain-specific workflows, mentoring engineers and shaping production tools and dashboards.