Lead Research Engineer, Data Quality

Clera

United States

On-site

Who this role is best for

Strong fit for candidates with deep technical expertise in AI data evaluation who can lead ambiguous projects and translate research into production systems.

Best fit for

Candidates with 5+ years in AI data evaluation and a track record of leading ambiguous technical projects
— “Experience leading technical projects or teams through ambiguous problems, from definition to implementation and iteration.”
Individuals who have built AI or machine learning data evaluation systems and can shape research practices
— “At least 5 years of research or engineering experience, including building AI or machine learning data evaluation systems.”
Professionals with a background in synthetic data validation and a clear understanding of agent training data requirements
— “Background in AI evaluations, post-training, synthetic data validation, benchmarks, or machine learning data quality.”

Things to consider

On-site presence in the United States is required despite the startup's early-stage nature
— “On-site in the United States.”
The role involves translating research into production systems, which may demand high technical execution and cross-functional coordination
— “Turn research insights into production tools, dashboards, validation pipelines, and feedback loops.”

How to stand out

Highlight experience in defining data quality standards and designing validation experiments
— “Set data quality strategy by defining standards, building quality control systems, and designing experiments to assess agent outputs.”
Showcase your ability to work with domain experts and improve data generation workflows
— “Work with research engineers, subject-matter experts, and data providers to investigate quality issues and improve data generation workflows.”

Pace · SteadyCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

lead development of systems to evaluate tasks
set data quality strategy
develop scalable synthetic data validation methods

Typical background

5 years of research or engineering experiencebackground in AI evaluations

Skills & requirements

Required

AI EvaluationsPost-trainingSynthetic Data ValidationMetrics DesignQuality ProcessesResearch InsightsProduction ToolsValidation PipelinesFeedback Loops

Preferred

Reinforcement LearningBenchmarkingDomain-specific Workflows

Stack & domain

PythonDockerLinuxAI EvaluationsPost-trainingSynthetic Data ValidationBenchmarksMachine Learning Data QualityMetrics DesignQuality ProcessesResearch InsightsProduction ToolsDashboardsValidation PipelinesFeedback LoopsTechnical RigorClear CommunicationEarly-stage Startup EnvironmentAIMachine LearningData QualityTraining DataReinforcement LearningSynthetic DataDomain-specific Workflows

About the role

Original posting from Clera via Ashby

ABOUT THE ROLE
Lead the data quality team at an early-stage AI evaluation infrastructure startup. You will shape the systems and research practices used to measure and improve training data for AI agents, helping ensure it is useful, reliable, and representative of real-world tasks.
WHAT YOU'LL DO
Lead development of systems to evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.
Set data quality strategy by defining standards, building quality control systems, and designing experiments to assess agent outputs.
Develop scalable synthetic data validation methods, including failure analysis, task mutation checks, and trajectory audits.
Work with research engineers, subject-matter experts, and data providers to investigate quality issues and improve data generation workflows.
Turn research insights into production tools, dashboards, validation pipelines, and feedback loops.
Mentor research engineers and promote technical rigor, clear communication, and effective execution.
WHAT WE'RE LOOKING FOR
At least 5 years of research or engineering experience, including building AI or machine learning data evaluation systems.
Experience leading technical projects or teams through ambiguous problems, from definition to implementation and iteration.
Advanced proficiency in Python, Docker, and Linux.
Background in AI evaluations, post-training, synthetic data validation, benchmarks, or machine learning data quality.
Strong judgment about what makes agent training data realistic, learnable, diverse, reliable, and useful.
Experience designing metrics and quality processes, translating research into production systems, and working with domain experts.
Clear written communication and experience in an early-stage startup environment.
COMPENSATION & BENEFITS
Annual salary range: $130,000 to $225,000 USD. Visa sponsorship is available.
LOCATION
On-site in the United States.

Source: Clera careers (Ashby)