Senior Machine Learning Engineer - Messaging Platform
London
- Pay
- Pay not listed
- Work setting
- Not stated
- 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 postingDesign, build and ship ML models that optimize messaging across push, email and in-app
Plan and run A/B experiments balancing conversion, engagement, retention and reachability
Contribute to reinforcement learning systems optimizing long-term user outcomes
Own the full ML lifecycle from modeling to deployment, monitoring and iteration
Integrate ML models with upstream domain value signals and opportunity generation
From the employer’s posting
Design, build, and ship machine learning models that optimize messaging across push, email, and in-app channels
Plan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability
Contribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions
Own the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration
Integrate ML models with upstream systems, including domain value signals and opportunity generation frameworks
What you’ll bring
All qualificationsCore experience
- You have strong experience building and deploying machine learning models in production environments at scale
- You bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks
- You have experience with or curiosity about reinforcement learning and long-term optimization systems
Qualification wording
You have strong experience building and deploying machine learning models in production environments at scale
You bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks
You have experience with or curiosity about reinforcement learning and long-term optimization systems
Tools in this posting
- PyTorch
Source — Tool mentions in context
- You have worked on complex optimization problems such as ranking systems or multi-objective decision-making - You bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks - You understand experimentation deeply and can design reliable tests in environments with interacting metrics
Posting record
Report an errorOpen 5 monthsWe first saw this posting 171 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- May 11, 2026 · 171 days ago
- Recorded sightings
- 207
- Last seen by us
- Oct 30, 2026
- Employer says posted
- May 8, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
The Messaging Platform powers Spotify’s communications to over a billion users — from push notifications to emails and in-app messages that connect listeners to the content they love. Within this space, the Paloma squad focuses on message optimization: deciding which message reaches which user, through which channel, and at what moment.
We’re evolving how messaging works at Spotify — moving from short-term optimization toward systems that understand long-term user journeys. By combining reinforcement learning approaches with deeper domain signals, we’re expanding how machine learning shapes the entire messaging funnel.
What You'll Do
- Design, build, and ship machine learning models that optimize messaging across push, email, and in-app channels
- Plan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability
- Contribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions
- Partner with product managers, data scientists, and engineers to define what success looks like and how to measure it
- Own the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration
- Integrate ML models with upstream systems, including domain value signals and opportunity generation frameworks
- Help shape the future of AI-assisted development within the team, exploring how tools can accelerate experimentation and delivery
Who You Are
- You have strong experience building and deploying machine learning models in production environments at scale
- You are comfortable translating business problems into ML solutions and discussing trade-offs with cross-functional partners
- You have worked on complex optimization problems such as ranking systems or multi-objective decision-making
- You bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks
- You understand experimentation deeply and can design reliable tests in environments with interacting metrics
- You are able to analyze results using approaches like causal inference or metric decomposition when needed
- You have experience with or curiosity about reinforcement learning and long-term optimization systems
- You enjoy working across disciplines and navigating ambiguity while shaping strategy and direction
Where You'll Be
- This role is based in London and Stockholm
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
Employment type
Permanent
Source details
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
London
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