Senior Engineering Manager, Machine Learning
Lead a machine-learning team building fraud-detection models for e-commerce, balancing short-term model improvements with long-term research while mentoring engineers and translating ML metrics into business outcomes.
- Compensation
- $200K–$245K / yr
- Location
- United States (Remote);
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- You are a hands-on Player-Coach manager who thrives in ambiguity—where the roadmap is a set of hypotheses, and the answer to "will this work?" is "we'll know in three weeks."
- You bring:
- Technical Credibility (The "Player"): You stay close enough to the work to have a grounded opinion. You read the code, inspect evaluation pipelines, and can immediately tell the difference between a s
- Leadership & Rigor (The "Coach"): You hold a high bar for evidence without becoming a bottleneck to experimentation. You mentor engineers to own their code quality, and you translate complex ML perfor
- Executive Judgment: You know how to balance research bets against quarterly delivery, disagree and commit when decisions are made, and build an environment where well-documented negative experimental
- Roughly 5+ years in machine learning, data science, or ML-adjacent software engineering, including at least 3 years of people management — guiding career development, addressing conflicts, and buildin
- Genuine depth in at least one of engineering and applied statistics, and real working competence in the other. We are not hiring a manager of analysts, and we are not hiring a manager of a pure softwa
- Demonstrated ability to lead work under real uncertainty: setting a direction when the answer isn't known yet, changing course when evidence says to, and communicating both without eroding your team's
- Excellent written and verbal communication. Much of our decision-making happens in documents, and we expect managers to write well.
- Autonomy in recognizing priorities and evaluating the impact of outcomes, and comfort working without close supervision in a fast-moving environment.
- Commitment to quality. You take pride in work that excels in correctness, reproducibility, and reliability, and you set that standard for your team.
Full description
At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy.
Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here!
Signifyd AI Lab (SAIL) builds the ML products behind Signifyd's fraud and risk decisions. We improve the predictive performance of the models that decide e-commerce transactions at scale, we scale the ML capabilities of our Risk organization, and we push into the new markets and problem spaces that expand the market Signifyd can sell to.
Every space in this department is a mix of experimentation, code, and statistics. We don't create walls between the people who have the ideas and the people who build them. The team splits its time between near-term continuous model improvements and longer-horizon innovation bets to improve the company’s capabilities in 2027 and beyond. These bets surface from the ground up in an environment where we believe those closest to the problems are best placed to understand how to solve them.
We’re hiring a manager to lead one of the teams in this department.
Who You Are
You are a hands-on Player-Coach manager who thrives in ambiguity—where the roadmap is a set of hypotheses, and the answer to "will this work?" is "we'll know in three weeks."
You bring:
Technical Credibility (The "Player"): You stay close enough to the work to have a grounded opinion. You read the code, inspect evaluation pipelines, and can immediately tell the difference between a statistical result that will hold up in production and one that just happened to look good on a single test window.
Leadership & Rigor (The "Coach"): You hold a high bar for evidence without becoming a bottleneck to experimentation. You mentor engineers to own their code quality, and you translate complex ML performance metrics into clear business outcomes for Risk leadership.
Executive Judgment: You know how to balance research bets against quarterly delivery, disagree and commit when decisions are made, and build an environment where well-documented negative experimental results are celebrated as real progress.
What You'll Do
Lead and grow the team
- Guide career development, manage conflicts, and nurture a positive work environment.