Research Engineer

Summary

HUD is building infrastructure to create RL training data and evaluations for frontier AI agents, and they are seeking research engineers to help build out quality assurance for this training data. The role involves defining quality standards, building tooling and workflows for auditing datasets, and partnering with data vendors to improve data generation processes.

Responsibilities

• Define and enforce quality standards for training data
• Build tooling and workflows to audit supplier-generated datasets, including sampling strategies, validation pipelines (rule-based and model-assisted), and feedback loops
• Determine if and how human-in-the-loop review workflows can be used to optimize QA
• Partner with data vendors to debug quality issues, provide actionable feedback, and improve their data generation processes
• Continuously integrate QA learnings into infrastructure tools and data vendor portal to reduce anomalies, inconsistencies, and edge cases

Skills

• Proficiency in Python, Docker, and Linux environments
• Worked with large-scale datasets
• Evidence of rapid learning and adaptability in technical environments (e.g., programming competitions)
• Startup experience in early-stage technology companies with ability to work independently in fast-paced environments
• Familiarity with current AI tools and LLM capabilities
• Strong communication skills for remote collaboration across time zones
• Understand of common failure modes in training data
• Have experience building data validation pipelines and/or human-in-the-loop review systems
• Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
• Be comfortable designing metrics, experiments, and QA processes, not just executing them

Qualifications

Must Haves

• Proficiency in Python, Docker, and Linux environments
• Worked with large-scale datasets
• Evidence of rapid learning and adaptability in technical environments (e.g., programming competitions)
• Startup experience in early-stage technology companies with ability to work independently in fast-paced environments
• Familiarity with current AI tools and LLM capabilities
• Strong communication skills for remote collaboration across time zones

Nice to Haves

• Understand of common failure modes in training data
• Have experience building data validation pipelines and/or human-in-the-loop review systems
• Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
• Be comfortable designing metrics, experiments, and QA processes, not just executing them

Benefits

• Visa Sponsorship : We provide support for relocation and visas for strong full-time candidates to the US.
• 100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA
• Lunch and dinner when you’re in the office
• 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
• Unlimited access to tokens for ChatGPT, Claude Code, Cursor, etc.