Pay
Higher than most similar jobs
$130K–$225K against $105K–$170K, the middle half of what similar US postings state.
From 109 postings in the last year. Stated ranges, not salaries paid.
Lead Research Engineer, Data Quality
United States
Actively hiring, first seen by us 4 hours ago- Pay
$130,000–225,000/year · Base — pay source
Compensation & Benefits Base salary: $130,000 to $225,000 annually. Visa sponsorship is available. Location
Read the full posting- Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting
Before you apply
- Work authorization
- Sponsors visas
What you’ll work on
Full postingLead the data quality team at an early-stage AI company building infrastructure for reinforcement learning and model evaluation.
Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.
Develop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits.
Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation.
From the employer’s posting
Lead the data quality team at an early-stage AI company building infrastructure for reinforcement learning and model evaluation. You will shape how training data is assessed and improved, helping research teams create more reliable and useful AI agents.
What You'll Do Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows. Set data quality strategy, establish standards, and design experiments to assess agent outputs.
Set data quality strategy, establish standards, and design experiments to assess agent outputs. Develop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits. Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation.
Develop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits. 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.
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 11, 2026today
- Recorded sightings
- 1
- Employer says posted
- Nov 6, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 6 and day 54.
From 1,533 similar postings that came down. Coming down is not always a hire.
clera is hiring
16 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Docker
Where the posting names them
- Experience leading technical projects or teams through ambiguous problem definition, implementation, and iteration. - Advanced proficiency in Python, Docker, and Linux. - Background in AI evaluations, post-training, benchmarks, synthetic data pipelines, or model evaluation infrastructure.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
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Demand for Data Governance roles
New Data Governance postings at the employers we follow are up 196% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Governance, by the work the posting describes.
Data governance staff keep a company's data accurate, documented and used within the rules. They agree what key fields mean, set up quality checks, track who owns each dataset and look after master data.
Postings ask for SQL, a governance tool such as Collibra or Informatica, knowledge of privacy and data rules, and a lot of work with other teams.
A data engineer builds the systems that move data; governance staff set and check the standards that data has to meet.
Job description
About the Role
Lead the data quality team at an early-stage AI company building infrastructure for reinforcement learning and model evaluation. You will shape how training data is assessed and improved, helping research teams create more reliable and useful AI agents.
What You'll Do
Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.
Set data quality strategy, establish standards, and design experiments to assess agent outputs.
Develop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits.
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 promote technical rigor, clear communication, and strong execution.
What We're Looking For
At least 5 years of research or engineering experience focused on AI/ML data evaluation or data quality systems.
Experience leading technical projects or teams through ambiguous problem definition, implementation, and iteration.
Advanced proficiency in Python, Docker, and Linux.
Background in AI evaluations, post-training, benchmarks, synthetic data pipelines, or model evaluation infrastructure.
Strong judgment about what makes agent training data realistic, learnable, diverse, reliable, and useful.
Experience designing metrics, experiments, and quality processes, and translating research findings into production systems.
Ability to work with subject-matter experts and convert domain judgment into scalable review or generation workflows.
Strong written communication, 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. The primary city was not specified.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Compensation & Benefits Base salary: $130,000 to $225,000 annually. Visa sponsorship is available. Location
- Location & working pattern
United States
Location On-site in the United States. The primary city was not specified.
- Work authorization
Compensation & Benefits Base salary: $130,000 to $225,000 annually. Visa sponsorship is available. Location