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SoundCloud is seeking an Engineering Manager to lead the Ads ML team within the Growth, Revenue and Commerce group. This hybrid leadership role combines hands-on technical contribution with team management, overseeing four backend and ML engineers while driving SoundCloud's ad delivery into the machine learning era.
Advertising is a core revenue stream for SoundCloud, directly funding creators on the platform and enabling free listeners to discover paid subscriptions. The team builds systems that determine which ads listeners hear, how many, and when. The current initiative replaces fixed rules with ML-driven approaches: contextual bandits and predictive models (churn risk, subscription propensity, user LTV) that set ad load for each listener in real time. The goal is to increase ad revenue without harming retention.
Key responsibilities include owning the ML roadmap for ad delivery, taking personalized ad serving from experiment to production across markets; owning the production ML stack (online model serving, real-time bandit decisioning, feature pipelines, model evaluation and monitoring); building a rigorous experimentation practice with offline/online evaluation, A/B test design, and guardrail metrics; owning backend ad delivery services that turn model decisions into ads served on iOS, Android and web; partnering with client engineers, data analysts and product on roadmap priorities; and growing the team's ML depth through hiring, mentorship and performance management.
You'll report to the Director of Engineering in a flat structure and work closely with cross-functional stakeholders to balance revenue, retention and ad inventory.
SoundCloud operates offices in New York, Los Angeles, Berlin, and London, with a flexible hybrid culture supporting both in-office collaboration and remote work.
Requirements:
- 2+ years of engineering management experience
- 5+ years as a backend software developer; comfortable writing code, discussing trade-offs and participating in system design
- Hands-on experience building and running ML systems in production: model serving, real-time decisioning, feature pipelines, monitoring for model drift and data quality
- Solid grasp of applied ML for decision-making (bandits, ranking, recommendation or propensity models), deep enough to challenge modelling choices and trade-offs
- Strong experiment design skills, including statistical rigour and understanding trade-offs between short-term revenue and long-term retention
- Experience managing engineers, including coaching, feedback and performance development
- Clear communication with technical and non-technical stakeholders, explaining ML trade-offs in business terms
Bonus experience: ad tech, ad serving or large-scale experimentation on consumer platforms; contextual or multi-armed bandits, reinforcement learning or uplift modelling in production; ML platform tooling (feature stores, model registries, workflows); user LTV, churn or subscription-propensity modelling; ad standards and integrations (VAST, programmatic demand, frequency capping); audio or video advertising; Scala or JVM languages and Python for ML.
