Remote Senior ML Engineering Manager - Risk & Fraud

Signifyd; Northern (KY); Hybrid; USD 210,000 - 245,000; Full time

Benefits offered by this job

401K Match

Stock Options

Annual Performance Bonus

Paid Parental Leave

Health Insurance

Vision Insurance

FSA

Disability Insurance

Life Insurance

Company Social Events

Signifyd Swag

Job summary

Signifyd is hiring a Senior Engineering Manager for Machine Learning. This remote US-based role leads one of Signifyd AI Lab teams focused on fraud and risk decisions, with strong emphasis on practical production ML, measurement, and risk collaboration.

You will mentor engineers, guide research bets, and drive a pipeline from hypotheses to production, balancing speed with robust, testable results in a distributed organization.

Qualifications

• 5+ years in machine learning, data science, or ML-adjacent software engineering, including at least 3 years of people management.
• Deep technical depth in at least one engineering or applied statistics area and working competence in the other.
• Ability to lead work under real uncertainty, set direction, and defend decisions with evidence.

Responsibilities

• Lead and grow the ML engineering team, including career development and performance coaching.
• Set clear goals, provide feedback, and ensure a collaborative, high-performing environment.
• Partner with Risk and other stakeholders to align model performance with business thresholds and outcomes.
• Guide roadmaps, allocate resources, and balance research bets with quarterly delivery.
• Represent the team to leadership, defend decisions, and ensure end-to-end delivery of experiments to production.

Skills

Machine learning

People management

Technical leadership

Experimentation

Job description

Signifyd is hiring a Senior Engineering Manager for Machine Learning. This remote US-based role leads one of Signifyd AI Lab teams focused on fraud and risk decisions, with strong emphasis on practical production ML, measurement, and risk collaboration.

You will mentor engineers, guide research bets, and drive a pipeline from hypotheses to production, balancing speed with robust, testable results in a distributed organization.