ML Engineering Manager

ML Engineering Manager

Responsibilities:

Lead and grow the team

• 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.
• Operate with a high degree of autonomy. Our direction comes from measured performance against those commitments and from what our own experiments tell us, not from a product backlog handed to the team. You are expected to know what your team should be working on and to defend it, rather than wait to be told.
• Partner with our platform and infrastructure engineering teams on the feature systems, training pipelines, and experimentation tooling your team depends on — and be clear about where the boundary sits between what SAIL should own and what belongs to Engineering.
• Represent your team's results to a broad audience: engineering leadership, Risk leaders, and the wider company.

Requirements:

• 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 building a healthy, high-performing team.
• 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 software team. Our engineers train production models that decide serious traffic, and we expect their manager to be able to engage with that work at a technical level.
• 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 confidence.
• 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.

Benefits:

• Stock Options
• Annual Performance Bonus or Commissions
• Pension matched up to 3%
• ‘Day one’ access to great health insurance scheme
• Paid team social events
• Mental wellbeing resources