Lead Research Engineer, Data Quality

Навыки

• Коммуникация
• Data Quality
• Docker
• Linux
• Machine Learning
• Python

О компании и продукте

• We're an early-stage AI/ML infrastructure company (11–50 people) building the platform that AI labs and businesses use to create, manage, and scale reinforcement learning (RL) environments and high-quality post-training datasets. We're looking for a Lead Research Engineer, Data Quality to own the strategy and systems that measure, improve, and scale training data for frontier agents.
• In this role, you'll lead the data quality team, shape our internal research culture, and define what makes agent training data truly useful — not just superficially correct. You'll work on-site in San Francisco, CA. Visa sponsorship is available.

Задачи

• Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows
• Define the company's data quality strategy — build QC systems, enforce standards, and design experiments to grade agent outputs
• Develop novel methods for validating synthetic data at scale, including failure-mode analysis, task mutation checks, and trajectory auditing
• Partner with research engineers, domain experts, and data 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 build internal research taste around what makes agent training data realistic, learnable, diverse, reliable, and useful
• Mentor research engineers to maintain a high bar for technical rigor, clarity, and execution speed

Требования

• Required (dealbreakers)
• 5+ years of experience in a research or data quality engineering role, specifically building systems for AI/ML data evaluation
• Demonstrated experience leading technical projects or teams in data quality or AI/ML evaluation
• Advanced proficiency in Python, Docker, and Linux environments
• Also required
• Ability to reason deeply about characteristics of high-quality training data (realistic, learnable, diverse, reliable) for AI agents
• Experience translating research insights into production systems and data pipelines (e.g., validation pipelines, feedback loops)
• Experience developing and implementing methods for validating synthetic data at scale
• Experience collaborating with research engineers, domain experts, and data vendors to diagnose quality issues
• Track record mentoring engineers on technical rigor and execution speed
• Comfort navigating complex systems involving domain experts, vendors, generated data, model outputs, graders, and infrastructure

Будет плюсом

• Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration
• Experience working with subject-matter experts to convert domain judgment into scalable review or generation systems
• Experience designing metrics, experiments, and QA/QC processes from scratch
• Prior early-stage startup experience and comfort operating independently in fast-paced environments
• Strong written communication skills — able to explain methodology clearly to researchers, engineers, and external audiences

Условия

• Salary: $150,000 – $250,000 USD annually, depending on experience
• Early-stage equity participation
• Visa sponsorship available
• LOCATION
• This is an on-site role based in San Francisco, CA. Candidates must be willing to work in-office. Visa sponsorship is available for qualified candidates

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