Why This Job is Featured on The SaaS Jobs
Personalization sits at the heart of many subscription SaaS products because it directly influences retention, engagement, and perceived product quality. This role is notable in the ecosystem because it focuses on recommendation systems tied to long term user satisfaction, not just short term clicks, and it operates in a mature, high scale environment where experimentation and model performance have clear product consequences.
For a SaaS career, the durable value here is learning how machine learning becomes a product capability rather than a research artifact. The work spans prototyping through productionization, plus evaluation and A/B testing, which are core loops in data driven SaaS development. Experience integrating mid term signals across multiple systems also translates well to other SaaS companies that rely on shared platforms, reusable models, and cross functional alignment to ship improvements safely.
This position tends to suit senior engineers who like owning end to end ML systems and collaborating across research, data science, and engineering. It also fits professionals who prefer disciplined measurement, reliability, and iterative delivery, and who want their technical decisions to be constrained by real user behavior and product metrics rather than isolated benchmarks.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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, podcasts, and audiobooks 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 are looking for a Senior Machine Learning Engineer to join the Personalization team - an area of hardworking engineers that are passionate about understanding what drives user satisfaction with Spotify - and who make impactful changes to recommendation systems to achieve this goal. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction.
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What You'll Do- Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining ML models for the personalization of the main homepage
- Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems
- Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization.
Who You Are- You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with expertise in statistics and optimization, especially in sequential models, transformer architecture models, and fine-tuning processes for sequential models
- You have hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
- You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages
- Experience with TensorFlow, PyTorch, Scikit-learn, etc. is a strong plus
- You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS
- You care about agile software processes, data-driven development, reliability, and disciplined experimentation.
Where You'll Be- We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.
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The United States base range for this position is $176,166 $251,666 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
