ML Engineering Manager, Fraud & Risk – Remote (US)

Harnham; New York (NY); Remote; USD 200,000 - 245,000; Full time

Benefits offered by this job

Annual bonus (up to 10%)

Unlimited PTO

401k match

12 weeks paid parental leave

Learning budget

Job summary

Harnham is seeking an ML Engineering Manager for Fraud and Risk to lead a team of engineers in building scalable ML models for e-commerce fraud decisions. This remote US role emphasizes experiments, rigorous evaluation, and close collaboration with risk and engineering leadership.

You will guide 4–7 ICs, drive hypothesis evaluation, and balance continuous improvement with research bets while ensuring models perform well in production environments.

Qualifications

• 5+ years in ML, data science or ML-adjacent software engineering.
• At least two years of people management, ideally three or more.
• Genuine depth in engineering or applied statistics, with real working competence in the other.

Responsibilities

• Lead and grow a team of 4 to 7 through 1:1s, career development and hiring.
• Partner with tech leads to decide which hypotheses get resourced, iterated or closed.
• Balance annual improvement targets with research bets and re-prioritisation as evidence arrives.
• Ensure offline results hold up online and across rolling windows.
• Converge experiments into release candidates and ship on a regular cadence.
• Collaborate with Risk as the primary stakeholder and present results to engineering leadership.

Skills

ML

Data science

People management

Quantitative targets

Education

PhD or Master’s in CS or Statistics

Job description

Harnham is seeking an ML Engineering Manager for Fraud and Risk to lead a team of engineers in building scalable ML models for e-commerce fraud decisions. This remote US role emphasizes experiments, rigorous evaluation, and close collaboration with risk and engineering leadership.

You will guide 4–7 ICs, drive hypothesis evaluation, and balance continuous improvement with research bets while ensuring models perform well in production environments.