.png)
At Spotify, our Subscriptions Mission focuses on creating experiences that help listeners discover the value of Spotify, become subscribers, and stay with us for the long term. By combining technology, experimentation, and a deep understanding of our users, we help connect artists and fans while building a sustainable subscription business.
Within the Subscriptions Mission, the Subs Foundational Models Product Area is part of the Subs User Understanding Studio. We build scalable machine learning solutions that personalize the subscriber journey, from conversion through to long-term retention. Working closely with teams across the mission, we develop ML solutions that improve key business metrics, including subscriber growth, monthly active users, and Premium retention.
We're looking for a Machine Learning Engineer to join our Radius squad. You'll take ownership of impactful ML initiatives, develop and improve production models, and contribute to the foundational ML capabilities that power personalization and optimization across Subscriptions.
Your work could include reducing friction for Free users while balancing retention and conversion, improving payment experiences to reduce Premium churn, or developing reusable models such as subscriber embeddings that support multiple products and use cases.
What You'll Do
- Lead multi-week ML initiatives end to end, from translating business requirements into technical specifications to shipping models and measuring their impact on subscriber metrics
- Own components of our ML systems, making independent technical trade-offs and keeping them observable, reliable, and well documented
- Collaborate with a multi-functional agile team spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalised and relevant ways
- Prototype new approaches and productionise solutions at scale for our hundreds of millions of active users
- Help drive optimisation, testing, and tooling to improve quality
- Unblock and share context with your teammates to raise the whole squad's velocity
- Use AI-assisted engineering to move faster and remove repetitive toil, while owning the quality of everything you ship
- Partner closely with senior and staff ML engineers, and be part of an active group of machine learning practitioners in your mission and across Spotify
Who You Are
- You have experience building and shipping production ML systems
- You have a strong background in machine learning, theory, and practice
- You are comfortable explaining the intuition and assumptions behind ML concepts
- You can own well-scoped ML projects end to end and make sound technical decisions independently
- You communicate clearly with stakeholders and can explain the reasoning behind your technical choices
- You are curious, open to feedback, and keen to grow your scope over time
- You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languages, including model development with PyTorch
- You are experienced with building data pipelines, and you are self-sufficient in getting the data you need to build and evaluate your models
- You preferably have experience with cloud platforms like GCP or AWS
- You care about agile software processes, data development, reliability, and focused experimentation
- You have a desire to drive business impact
Where You'll Be
We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location
This team operates within the Central European and GMT time zone for collaboration.
Skills Required
- Experience building and shipping production machine learning systems
- Strong background in machine learning theory and practice
- Ability to explain the intuition and assumptions behind machine learning concepts
- Ability to own well-scoped machine learning projects end to end and make independent technical decisions
- Clear communication with stakeholders and ability to explain technical choices
- Hands-on experience implementing and maintaining production machine learning systems using Python, Scala, or similar languages
- Experience developing models with PyTorch
- Experience building data pipelines and independently obtaining data for model development and evaluation
- Experience with cloud platforms such as GCP or AWS
- Interest in agile software processes, data development, system reliability, and focused experimentation
- Desire to drive business impact
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
-
Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
-
Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
-
Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
Spotify Insights
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
