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!

ML Engineering Manager

Department: AI Lab/Machine Learning

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.

We’re hiring a manager to lead one of the teams in this department.

Who You Are

You are a hands-on Player-Coach 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."

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.

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.

Responsibilities

Guide career development, mentor the team, manage conflicts, and nurture a positive, collaborative environment across a geographically distributed organization.
Engage in regular 1:1s, give constant feedback, and create a safe environment for open discussion — including the discussions that follow an experiment that didn't work.
Encourage a culture of learning and improvement, provide technical guidance, and support team members in both technical and soft skills.
Identify and address gaps in team capabilities and processes to enhance team efficiency and success.

Run a portfolio of experiments, not a delivery queue

Partner with your tech leads, who own and drive the technical roadmap for their areas. Your job is not to be the sole source of ideas — it is to pressure-test them, sharpen them, make sure the strongest ones get resourced, and make sure the people generating them have the room and the support to do it.
Make the calls the roadmap can't make for you: which hypotheses get compute and headcount, which get another iteration, and which get a clear, documented "no." A well-run negative result is a real outcome, and we treat it as one
Manage the trade-off between a committed improvement target you must hit this year and research bets that may not pay off for several quarters. You will re-cut that budget as evidence arrives, and you'll be able to explain the reasoning to both your team and your stakeholders.
Bring rigor to how the team decides something worked. Offline results have to predict online behavior; a strong point estimate on a single evaluation window is a starting point, not a conclusion.
Own delivery on a cadence. Independent experimental workstreams have to converge into a release candidate, get evaluated end to end, and ship — including the hard call to leave a workstream out of a release when it isn't carrying its weight.

Set direction from data, in partnership with Risk

Work directly with our Risk partners as your primary stakeholders. Our commitments to them are explicit, measured, and written down; we deliver model performance, and they own thresholds, rules, and how decisions are applied to merchants.