HUD - Research Engineer
Requirements
• 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 • Strong candidates may also: • 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 • We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria. • Team & company details • Team Size: ~15 people currently, mostly full-time in-person, but some remote. • Team Size • Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc. • Our team: • Company stage: We have 8 figures in funding and high revenue growth. We’re scaling profitably and quickly to meet very strong demand. • Company stage: • Logistics • Logistics • Employment: Full-time. • Employment • Location: On-site in the San Francisco Bay Area. • Location • Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US. • Timeline: Applications are rolling. The process is 2 technical interviews and a 1-week work trial. • Timeline
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
Benefits
• 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. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is. • Due to high volume, we may not actively respond to every application, but feel free to contact us at [email protected] or elsewhere if we missed your application!
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