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Clera

Lead Research Engineer, Data Quality

United StatesOnsite · United States
Staff · FullTime · Engineering
POSTED 13 NOVVERIFIED 39M AGO
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Same title, closed before
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Posted 13 Nov · n=431
Payats field
$130k–225kposted
▼ −30% vs market median $255k
$130k$281k
Market p25–p75
$220k–$281k
Clera p25–p75
$160k–$206kn=25
United States · n=361 · 365d to 12 Nov
CompanyClera →
257open roles
Opened · 30d
+325
Closed · 30d
−352
13d of snapshots · 30d window
Market
992open · Staff ML / AI
Opened · 7d
+677% of open
All ml / ai
6,597open
All tracked boards · live 13 Nov · n=992
PythonDocker

About the Role

Lead the research engineering work focused on measuring and improving training data for AI agents. You will shape the strategy and systems used to evaluate data across reinforcement learning environments, synthetic datasets, benchmarks, and domain-specific workflows.

What You'll Do

Lead a team building systems to evaluate large volumes of tasks across environments, datasets, benchmarks, and specialized workflows.

Define data quality strategy by creating quality-control systems, setting standards, and designing experiments to assess agent outputs.

Develop scalable approaches to validate synthetic data, including failure-mode analysis, task mutation checks, and trajectory auditing.

Work with research engineers, domain experts, and data vendors to diagnose quality problems and improve data generation.

Turn research findings into production tools, dashboards, validation pipelines, and feedback loops.

Help establish rigorous standards for assessing whether agent training data is useful, and mentor research engineers.

What We're Looking For

At least 5 years of research or engineering experience building AI or machine learning data evaluation and quality systems.

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

Advanced proficiency in Python, Docker, and Linux environments.

Strong background in AI evaluations, post-training, synthetic data validation, benchmarks, or related evaluation infrastructure.

Sound judgment about what makes training tasks realistic, learnable, diverse, reliable, and useful.

Experience translating research insights into production systems and pipelines, and designing metrics, experiments, and quality processes.

Ability to work with subject-matter experts to turn domain judgment into scalable review or data generation systems.

Clear written communication, careful attention to detail, and experience in an early-stage startup environment.

Compensation & Benefits

Base salary: $130,000 to $225,000 annually. Visa sponsorship is available.

Location

On-site in the United States.

Source · CleraFirst seen 13 NovOriginal ↗