Why This Job is Featured on The SaaS Jobs
Work on personalization and rewards sits at the center of modern subscription SaaS, where retention and long-term engagement matter as much as acquisition. This Senior Machine Learning Engineer role is notable because it targets the mechanics behind discovery systems, specifically the reward signals that steer LLM-powered recommendations toward outcomes beyond short-term clicks. The focus on reinforcement learning and mid-term behavioral metrics reflects a broader shift in SaaS product analytics from immediate interaction to sustained user value.
For a SaaS career, the learning is highly portable: building production ML that connects experimentation to product surfaces, translating ambiguous product goals into measurable signals, and operating within A/B testing loops that influence core user journeys. Experience scaling pipelines and evaluation frameworks for recommendation systems also maps well to other SaaS domains where personalization, ranking, and lifecycle optimization are differentiators.
This role tends to suit engineers who enjoy bridging research and engineering, and who are comfortable being accountable for end-to-end model development through launch and iteration. It will appeal to professionals motivated by measurable product impact, cross-functional collaboration with data and research partners, and the discipline required to productionize RL methods in real user-facing systems.
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 Rewards team in Personalization (PZN) is defining the next generation of large-scale personalization at Spotify by pioneering novel Reinforcement Learning (RL) methods for Large Language Models (LLMs). We drive massive impact across all recommendations discovery surfaces by moving beyond simple clicks to model and optimize for true long-term user satisfaction. Our core mission is to bridge discovery with lasting listening habits by developing and deploying sophisticated, mid-term behavioral reward signals—such as retention and habit formation metrics—that directly shape the LLM-powered recommendation experience.
We are looking for a Machine Learning Engineer to make impactful changes to our recommendations and discovery algorithms. 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 reward signals for recommendations by hands-on ML development.
- Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation and discovery systems.
- Promote and role-model best practices of ML systems development, testing, and evaluation throughout the organization.
- In your first 6 months, you will be responsible for ML development: prototyping models, building pipelines, productizing/scaling models, and launching A/B tests for personalized generative recommendations at Spotify.
Who You Are
- You have a strong background in machine learning and enjoy applying theory to develop real-world applications.
- Reinforcement Learning (RL) expertise is key, and experience in RL for recommendations is a must have.
- Expertise in statistics and optimization, especially in sequential models, transformers, generative AI, large language models (LLMs are a plus), and relevant fine-tuning processes.
Where You'll Be
- This role is based in New York
- 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
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The United States base range for this position is $184,050- $262,928 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.
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.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
