Clera Lead Research Engineer, Data Quality at Clera responsible for building systems to evaluate tasks across RL environments and synthetic data. Own the strategy and systems that measure and improve training data quality.
Responsibilities
This is a senior technical leadership role on the data quality team at an early-stage AI infrastructure company focused on building and scaling reinforcement learning environments for frontier model training. You will own the strategy and systems that measure and improve training data quality, shaping research culture around what makes agent data genuinely useful rather than just superficially correct.
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 data quality strategy by building QC systems, enforcing standards, and designing experiments to grade agent outputs.
Develop new 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.
Turn qualitative research insights into production systems: internal tools, dashboards, validation pipelines, and feedback loops.
Mentor research engineers to maintain a high bar for technical rigor, clarity, and execution speed.
Qualification
Advanced proficiency in PythonExperience building QC systemsComfort designing metricsStrong written communication skills
Required
5+ years of relevant engineering or research experience, specifically building systems for AI/ML data evaluation or data quality.
Proven track record leading technical teams on ambiguous projects from problem definition through implementation and iteration.
Advanced proficiency in Python, Docker, and Linux environments.
Experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure.
Deep intuition for what makes training tasks realistic, learnable, diverse, reliable, and useful for AI agents.
Research-oriented understanding of AI evals and post-training, beyond surface-level agent tooling projects.
Comfort designing metrics, experiments, and QA/QC processes, not just executing them.
Strong written communication skills, with the ability to explain methodology clearly to diverse audiences.
Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems.
Early-stage startup experience and the ability to work independently in fast-paced environments.
