Lead Research Engineer, Data Quality

The role

You will lead the data quality team at an early-stage AI company. You will shape how training data is assessed and improved to help research teams create reliable AI agents.

What you will do

• Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.
• Set data quality strategy, establish standards, and design experiments to assess agent outputs.
• Develop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits.
• Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation.
• Turn research insights into production tools, dashboards, validation pipelines, and feedback loops.
• Mentor research engineers and promote technical rigor, clear communication, and strong execution.

Required qualifications

• At least 5 years of research or engineering experience focused on AI/ML data evaluation or data quality systems.
• Experience leading technical projects or teams through ambiguous problem definition, implementation, and iteration.
• Advanced proficiency in Python, Docker, and Linux.
• Background in AI evaluations, post-training, benchmarks, synthetic data pipelines, or model evaluation infrastructure.
• Strong judgment about what makes agent training data realistic, learnable, diverse, reliable, and useful.
• Experience designing metrics, experiments, and quality processes, and translating research findings into production systems.
• Ability to work with subject-matter experts and convert domain judgment into scalable review or generation workflows.
• Strong written communication, attention to detail, and experience in an early-stage startup environment.

Benefits

• Visa sponsorship is available

About the company

clera is an early-stage AI company building infrastructure for reinforcement learning and model evaluation.