Engineering Manager - Ads

Description

The company is the company's open audio platform, where creators, listeners and curators share and discover new music and audio.

The team is looking for an engineering manager to lead the Ads ML team within the Growth, Revenue and Commerce group and move ad delivery to machine learning.

Advertising is a core revenue stream. It earns directly, drives free listeners toward paid subscriptions, pays creators and covers content costs, so ad delivery affects listeners, artists and the business.

The team builds the systems that decide which ads listeners see, how many and when. Fixed rules are being replaced with ML, including contextual bandits and predictive models for churn risk, subscription propensity and user LTV that set ad load per listener in real time. The aim is higher ad revenue without losing retention, and you will set the pace.

This is a hybrid leadership role with substantial hands-on work. You will manage four backend and ML engineers, write code, and own the technical direction of ML-driven ad delivery. You will work with client engineers, data analysts and product, reporting to the Director of Engineering in a flat structure.

Offices are in New York, Los Angeles, Berlin and London. The company offers flexible working, with in-person collaboration at the offices and remote work from home.

The company is committed to diversity, equity and inclusion, and invests in its people through mentorship, workshops and learning opportunities.

Responsibilities

• Set the ML roadmap for ad delivery, moving personalised ad serving (contextual bandits, churn and subscription-propensity models) from experiment to production in every market, and decide what the team builds next.
• Run the production ML stack behind ad decisions: online model serving, real-time bandit decisioning, feature pipelines, and model evaluation and monitoring.
• Set up a disciplined ML experimentation practice covering offline and online evaluation, A/B test design, guardrail metrics, and clear ship or hold calls on ad load and user LTV.
• Own the backend services that turn model decisions into ads served on iOS, Android and web.
• Work with client engineers, data analysts and product to plan the roadmap, weighing revenue, retention and ad inventory against each other.
• Develop the team's ML skills through hiring, mentoring and career growth, and manage performance.

Requirements

• 2+ years of engineering management experience.
• 5+ years as a backend software developer, comfortable writing code, weighing trade-offs and contributing to system design.
• Hands-on experience building and running ML systems in production, including model serving, real-time decisioning, feature pipelines, and monitoring for model drift and data quality.
• Working knowledge of applied ML for decisions (bandits, ranking, recommendation or propensity models), enough to challenge modelling choices with the team.
• Strong experiment design skills, with statistical rigour and an understanding of short-term revenue versus long-term retention.
• Experience managing engineers through coaching, feedback and performance development.
• Ability to explain ML trade-offs to technical and non-technical stakeholders in business terms.
• Bonus points: experience in ad tech, ad serving or large-scale experimentation on a large consumer platform.
• Bonus points: contextual or multi-armed bandits, reinforcement learning or uplift modelling in production.
• Bonus points: ML platform tooling such as feature stores, model registries and workflow tools.
• Bonus points: user LTV, churn or subscription-propensity modelling.
• Bonus points: ad standards and integrations, including VAST, programmatic demand (SSPs and DSPs) and frequency capping.
• Bonus points: audio or video advertising.
• Bonus points: Scala or another JVM language, and Python for ML work.

Conditions

Hybrid role, combining work at one of the offices with working from home.

Equal-opportunity commitment: the company welcomes applicants regardless of background.

Benefits

• Creativity and Wellness allowance covering gym memberships, photography courses or books.
• Employee equity plan.
• Professional development allowance.
• Flexible vacation and public holiday policy, with up to 35 days of PTO per year.
• Snacks, treats and two free lunches a week at the office.