Engineering Manager - Ads

About SoundCloud

What's next in music happens on SoundCloud first. As the world’s largest open audio platform, SoundCloud is powered by a connected community of creators, listeners, and curators who share, discover, and influence what’s new, now, and next in music and audio.

We are looking for an Engineering Manager to lead our Ads ML (Machine Learning) team within the Growth, Revenue and Commerce group, and to take SoundCloud's ad delivery into the machine learning era.

Advertising is one of SoundCloud's core revenue streams. It earns money directly, and it is also one of the main ways free listeners discover and move to a paid subscription. That revenue pays creators on our platform, covers content costs, and keeps SoundCloud a healthy, independent company. Getting ad delivery right matters to listeners, artists, and the business.

Our team builds the systems that decide which ads SoundCloud listeners hear and see, how many, and when. We're replacing fixed rules with ML: contextual bandits and predictive models (churn risk, subscription propensity, user LTV) that set ad load for each listener in real time. The goal is more ad revenue without hurting retention, and you'll decide how far and how fast we get there.

This is a hybrid leadership role with a strong hands-on component. You'll lead a team of four backend and ML engineers, contribute directly to the codebase, and own the technical direction of machine learning driven ad delivery. You'll work closely with client engineers, data analysts, and product, and report to the Director of Engineering in a flat structure.

Key Responsibilities:

• Own the ML roadmap for ad delivery. Take personalised ad serving (contextual bandits, churn and subscription-propensity models) from experiment to production across markets, and decide what the team builds next.
• Own the production ML stack behind ad decisions: online model serving, real-time bandit decisioning, feature pipelines, and model evaluation and monitoring.
• Build a rigorous experimentation practice for ML: offline and online evaluation, A/B test design, guardrail metrics, and clear ship-or-hold decisions on ad load and user LTV.
• Own the backend ad delivery services that turn model decisions into the ads served on iOS, Android and web.
• Partner with client engineers, data analysts and product on the roadmap, balancing revenue, retention and ad in

• 1. Views the music industry purely through a major-label lens, ignoring the independent creator community.
• 2. Lacks tactical ideas for improving creator retention and monetization features.
• 3. Fails to grasp the technical challenges of handling massive audio streaming pipelines and low-latency delivery.

I scale two-sided audio marketplaces by aligning creator empowerment tools with data-driven fan discovery loops.