Engineering Manager – Ads

Overview

In this role you will lead the Ads ML team within Growth, Revenue and Commerce, guiding the shift to ML-driven ad delivery at scale. You’ll balance revenue goals with retention, working closely with cross-functional partners to ship production ML capabilities. You’ll shape the roadmap, implement real-time decisioning, and mentor a small team of backend and ML engineers in a flat, collaborative environment. This is a hands-on leadership position focused on impact, experimentation, and strategic technology choices that address ads, revenue and user experience.

Pay / Benefits

• Creativity and Wellness benefit
• Employee Equity Plan
• Professional development allowance
• Flexible vacation and public holiday policy (up to 35 days PTO)
• 2 free lunches weekly when at the office
• Snacks and goodies

Responsibilities

• Own the ML roadmap for ad delivery and translate experiments into production across markets
• Oversee production ML stack: online model serving, real-time decisioning, feature pipelines, monitoring
• Establish rigorous ML experimentation practices with offline/online evaluation and ship-or-hold criteria
• Manage backend ad delivery services that power ads on iOS, Android and web
• Collaborate with client engineers, data analysts and product to balance revenue, retention and inventory
• Grow ML depth through hiring, mentorship and performance management
• Contribute to codebase and define technical direction for ML-driven ad delivery

Key requirements

• 2+ years of engineering management experience
• 5+ years as a backend software developer
• Hands-on experience building and running ML systems in production
• Strong foundation in applied ML for decision-making (bandits, ranking, propensity models)
• Solid experiment design skills and understanding of revenue vs retention trade-offs
• Experience coaching, feedback and performance development for engineers
• Clear communication with technical and non-technical stakeholders

• leadership and people management
• communication with diverse stakeholders
• collaboration across cross-functional teams
• ML systems in production (model serving, real-time decisioning, feature pipelines, monitoring)
• contextual bandits and propensity models
• A/B test design and evaluation

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