Time since we first saw it
Day 13
Similar jobs usually come down between day 6 and day 70.
At Spotify, jobs usually come down by about day 39.
From 4,523 similar postings that came down, and 43 of Spotify’s. Coming down is not always a hire.
Machine Learning Engineer - Subscriptions
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Permanent — employment source
Employment type Permanent
Employment type in the employer’s posting: Permanent
From the employer’s posting
What you’ll work on
Full postingLead multi-week ML initiatives from business requirements to shipped models
Own components of ML systems and keep them observable, reliable, and documented
Prototype new approaches and productionise them at scale for hundreds of millions of users
Drive optimisation, testing, and tooling to improve quality
Build new product features with a cross-functional agile team
From the employer’s posting
Lead multi-week ML initiatives end to end, from translating business requirements into technical specifications to shipping models
Own components of our ML systems, making independent technical trade-offs and keeping them observable, reliable, and well documented
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
Collaborate with a multi-functional agile team spanning user research, design, data science, product management, and engineering to build new product features
What you’ll bring
All qualificationsCore experience
- You have experience building and shipping production ML systems
- You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languages, including model development with PyTorch
Qualification wording
You have experience building and shipping production ML systems
You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languages, including model development with PyTorch
Posting record
Report an errorActively hiringFirst seen by us 13 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 23, 202613 days ago
- Recorded sightings
- 19
- Last seen by us
- Nov 5, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Spotify is hiring
15 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is picking up
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Scala
- AWS
- GCP
- PyTorch
Where the posting names them
- 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 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
Your next step
Complete your application on jobs.lever.co. The employer’s form will show what is required.
Applying through Lever
- No account needed: one form, and Lever reads your resume into the profile recruiters search.
Already applied? Track this application
Has Spotify posted this job before?
Yes. We’ve followed one other Machine Learning Engineer - Subscriptions job there since May 2026. It came down after 12 days.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Employment type
Permanent
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
London
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.