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Singapore
$144K–$180K/yr
Published by the employer
The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we're behind some of Spotify's most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them.
We're looking for a Technical Program Manager to help bring some of our most meaningful machine learning and personalisation work to life. You'll lead cross-functional programs that shape how hundreds of millions of listeners discover music, podcasts, and audiobooks. You'll be the connective tissue between teams who turn billions of daily listening signals into recommendations that feel personal to every listener.
Lead end-to-end program management for large-scale ML and personalization initiatives, bringing together engineering, data science, product, and design.
Drive the delivery of recommendation systems and personalization models used by listeners around the world, with clear accountability for outcomes.
Own program roadmaps, milestones, dependency maps, and risk plans, identifying risks early and working with teams to resolve them before they become blockers.
Orchestrate ML development workflows, including model training pipelines, experimentation, data infrastructure, and production deployment.
Partner with engineering leaders on capacity planning, resourcing, and technical trade-off decisions.
Drive alignment across ML engineering, data science, product, platform, and UX teams so everyone is working toward the same goals.
Set up program metrics, reporting rhythms, and dashboards that give leadership clear visibility into progress, risks, and decisions.
Keep leadership well-informed with no surprises through clear, data-informed communication, and help improve how teams work across the ML lifecycle.
You have 8+ years of experience in technical program management, including meaningful time on ML, AI, data platforms, or similarly complex technical work.
You have a track record of delivering multi-team technical programs from early planning through production launch, and you hold yourself accountable for results.
You understand ML development workflows such as model training, experimentation, feature engineering, data pipelines, and production inference. You don't need to be an ML engineer yourself.
You build trusted relationships with engineering and product leaders, especially in distributed, cross-functional organizations.
You communicate clearly and can turn technical work into stories that make sense to everyone from engineers to executives.
You're comfortable with ambiguity and create clarity that helps teams with different priorities and timelines find a shared path forward.
You lead through influence rather than authority, and you care about the people and teams you work with.
You enjoy helping others grow and bring curiosity and openness to how programs can run better.