Lead Research Engineer, Data Quality

Clera; United States; On-site; USD 130,000 - 225,000; Full time

Benefits offered by this job

Visa sponsorship available

On-site role in the United States

Job summary

Clera is seeking a lead to steer the data quality team at an early-stage AI company. You will shape how training data is evaluated across reinforcement learning environments and synthetic datasets, building scalable validation systems and dashboards.

You will mentor engineers, define metrics, and drive production tools to improve data generation and quality across multiple workflows. On-site in the United States with visa sponsorship available.

Qualifications

• 5+ years of research or engineering experience building AI/ML data evaluation or quality systems.
• Advanced proficiency in Python, Docker, and Linux.
• Experience leading technical projects or teams through implementation.
• Understanding of AI evaluations and data realism for agent training.
• Experience validating synthetic data at scale and scalable workflows.
• Clear written communication and ability to work independently in a startup.

Responsibilities

• Set data quality strategy and build systems to evaluate thousands of tasks across diverse data sources.
• Develop scalable synthetic data validation methods, including failure-mode analysis and trajectory auditing.
• Design metrics, experiments, and quality standards for agent outputs.
• Collaborate with researchers, domain experts, and data vendors to diagnose quality issues.
• Turn research insights into production tools, dashboards, and validation pipelines.
• Mentor researchers and strengthen technical rigor across the team.

Skills

Python

Docker

Linux

Technical leadership

AI evaluations

Synthetic data validation

Data quality systems

Communication

Tools

Docker

Linux

Job description

About the Role

Lead the data quality team at an early-stage AI company, building systems to measure and improve training data for AI agents. Your work will shape how teams evaluate data across reinforcement learning environments, synthetic datasets, benchmarks, and domain-specific workflows.

What You'll Do

• Set data quality strategy and build systems to evaluate thousands of tasks across varied data sources and workflows.

• Develop scalable synthetic data validation methods, including failure-mode analysis, task mutation checks, and trajectory auditing.

• Design metrics, experiments, quality standards, and processes for evaluating agent outputs.

• 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 strengthen technical rigor across the team.

What We're Looking For

• At least 5 years of research or engineering experience building AI/ML data evaluation or quality systems.

• Advanced proficiency in Python, Docker, and Linux.

• Experience leading technical projects or teams from problem definition through implementation and iteration.

• Strong understanding of AI evaluations, post-training, and the qualities that make agent training data realistic, learnable, diverse, reliable, and useful.

• Experience validating synthetic data at scale and translating domain expertise into scalable review or generation workflows.

• Clear written communication and comfort working independently in an early-stage environment.

Compensation & Benefits

Annual salary range: $130,000 to $225,000. Visa sponsorship is available.

Location

On-site in the United States. Singapore is also listed as a work location.