Lead Research Engineer, Data Quality

Clera; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Equity participation

Visa sponsorship

Relocation assistance

Job summary

Clera in San Francisco, CA is hiring a Lead Research Engineer, Data Quality to own strategy and systems for measuring, improving, and scaling training data for frontier agents in RL environments. This high-impact, hands-on leadership role shapes both technical direction and research culture.

You will lead a data quality team, build QC pipelines, validate synthetic data at scale, and collaborate with researchers and data vendors to deliver reliable, diverse datasets used to train next-generation

Qualifications

• 5+ years of engineering or research experience.
• Proven track record leading technical teams on ambiguous projects.
• Advanced proficiency in Python, Docker, and Linux environments.
• Hands-on experience building QC systems, evals, benchmarks, or validation workflows.
• Deep intuition for data quality in training data.
• Strong written communication for researchers and engineers.
• Experience with startup environments and independent work.

Responsibilities

• Lead the data quality team in building evaluation systems for RL tasks.
• Define data quality strategy, QC systems, standards, and experiments.
• Develop scalable validation for synthetic data and trajectory auditing.
• Collaborate with researchers, domain experts, and data vendors to diagnose quality issues.
• Translate research insights into production tools, dashboards, and validation pipelines.
• Mentor engineers, upholding rigor, clarity, and execution speed.

Skills

Data quality

Leadership

Python

Docker

Linux

Communication

Independent work

Attention to detail

Tools

QC systems

Benchmarks

Synthetic data pipelines

Validation workflows

Model evaluation infra

Job description

About the Role

Join an early-stage AI/ML startup building the infrastructure layer for reinforcement learning environments and post-training data at the frontier. As Lead Research Engineer, Data Quality, you will own the strategy and systems that measure, improve, and scale training data for frontier agents. This is a high-impact, hands-on leadership role where you’ll shape both the technical direction and internal research culture around what makes agent training data truly useful.

The company is a well-funded, rapidly growing AI infrastructure platform (Series A/B stage) focused on RL environment tooling, synthetic data generation, and model evaluation — working directly with AI labs and research teams.

What You'll Do

• 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.
• Help build internal research intuition around what makes agent training data realistic, learnable, diverse, reliable, and useful — not just superficially correct.
• Mentor other research engineers, maintaining a high bar for technical rigor, clarity, and execution speed.

What We're Looking For

Required

• 5+ years of relevant engineering or research experience.
• Proven track record leading technical teams on ambiguous projects from problem definition through implementation and iteration.
• Advanced proficiency in Python, Docker, and Linux environments.
• Hands-on experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure.
• Deep intuition for data quality — what makes training tasks realistic, learnable, diverse, reliable, and useful.
• Comfort designing metrics, experiments, and QA/QC processes, not just executing them.
• Strong written communication; ability to explain methodology clearly to researchers, engineers, and external audiences.
• Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems.
• Early-stage startup experience with demonstrated ability to work independently in fast-paced environments.
• Detail-oriented mindset with an eye for subtle inconsistencies or edge cases in data.

Nice to Have

• Background in reinforcement learning, reward modeling, or agent evaluation.
• Experience shipping production research infrastructure (not just prototypes).
• Familiarity with large-scale task execution systems or distributed evaluation pipelines.

Compensation & Benefits

• Salary: $150,000 – $250,000 USD annually, commensurate with experience.
• Equity participation in an early-stage, high-growth AI company.
• Visa sponsorship available.

Location

This is an on-site role based in San Francisco, CA. Candidates should be willing and able to work from the office. Relocation support may be available for strong candidates.