[All remote jobs](https://jobicy.com/jobs.md)[![Spotify logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/03/Jobicy-210328105941-289246.jpg)](https://jobicy.com/company/spotify.md)[Spotify](https://jobicy.com/company/spotify.md) # Research Scientist – Personalization Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/spotify.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceOpen levelPublished13 Oct 2026Apply before12 Nov 2026Listing views32Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Spotify is seeking a Research Scientist to advance machine learning and AI for large-scale personalization across music and podcast experiences. The role emphasizes foundation models, generative AI, post-training, reinforcement learning, reward design, distributed training, and model evaluation. The scientist will conduct original research, build and validate novel methods, publish in leading research venues, and influence long-term product direction. This London-based position combines rigorous research with cross-functional collaboration and mentorship in a flexible hybrid work environment. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a highly advanced research role requiring a strong publication record and deep practical expertise in modern large-scale machine learning. Success depends on independently defining open-ended research problems, conducting rigorous experiments, and translating findings into strategic product opportunities. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$190,000US market range$150k–$240k0$264k AI insightNo salary was disclosed in the posting. This is an estimated US annual base-salary market range for a senior-level machine learning research scientist specializing in foundation models, generative AI, and reinforcement learning; actual London compensation may differ and may include bonus or equity. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Artificial Intelligence](https://jobicy.com/jobs?search_keywords=Artificial%20Intelligence.md)[Foundation Models](https://jobicy.com/jobs?search_keywords=Foundation%20Models.md)[Generative AI](https://jobicy.com/jobs?search_keywords=Generative%20AI.md)[Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md)[Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[Distributed Training](https://jobicy.com/jobs?search_keywords=Distributed%20Training.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Research Publications](https://jobicy.com/jobs?search_keywords=Research%20Publications.md) Sample interview questionsDescribe a research project in which you developed or materially improved a machine learning method.I would explain the research question, limitations of existing approaches, the proposed method, experimental design, datasets and baselines, and measurable results. I would also discuss negative findings, reproducibility steps, and how the work informed either a publication or a production-oriented next step. How would you evaluate whether a generative personalization model improves the listener experience? I would use a layered evaluation strategy: offline metrics for relevance, diversity, novelty, calibration, and safety; qualitative error analysis; and carefully designed online experiments measuring engagement and longer-term satisfaction. I would include guardrails to detect harmful concentration effects, feedback loops, and regressions for important listener segments. What are key challenges when applying reinforcement learning or post-training to recommendation and personalization? Important challenges include defining rewards that reflect durable user value rather than short-term clicks, handling biased logged feedback, maintaining exploration safely, and avoiding reward hacking. I would address these through counterfactual evaluation where appropriate, constrained objectives, robust offline validation, and staged online experimentation. Tell us about a time you translated an ambiguous research idea into a rigorous experimental plan. I would start by making the hypothesis and success criteria explicit, then identify datasets, baselines, ablations, and statistical methods before implementation. I would prioritize the highest-information experiments first, document assumptions, and revise the plan based on results while keeping stakeholders aligned on uncertainty and implications. How do you balance publishing novel research with delivering value to a product organization? I view the two goals as complementary when research agendas are tied to meaningful long-term product questions. I would maintain scientific rigor and publishable novelty while partnering early with product and engineering teams to identify feasible pathways, define decision-relevant evidence, and communicate both opportunities and limitations clearly. Opportunity details ## About this role. 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 and podcasts 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.Spotify’s Personalization organization builds the technology that helps millions of listeners discover what they love. Within this space, our research team focuses on advancing the state of the art in machine learning and AI to shape the future of personalization. We explore new approaches, challenge existing assumptions, and contribute to the broader research community while influencing long-term product direction. ### What You’ll Do * Conduct original research in machine learning and AI, with a focus on large-scale foundation models, generative AI, and post-training methods, including reinforcement learning. * Develop novel methodologies, models, and evaluation frameworks to advance personalization systems. * Design and execute rigorous experiments to explore new ideas and validate research hypotheses. * Contribute to the scientific community through publications, talks, and conference participation. * Collaborate with cross-functional partners to translate research insights into long-term product opportunities. * Help define and evolve a forward-looking research agenda aligned with Spotify’s personalization strategy. * Mentor others and contribute to a strong, curious, and collaborative research culture. ### Who You Are * You have a Master’s or PhD in machine learning, AI, or a related field, or equivalent research experience. * You have demonstrated expertise in machine learning and artificial intelligence through peer-reviewed publications at conferences such as NeurIPS, ICLR, ICML, KDD, or RecSys. * You have deep knowledge of machine learning, with hands-on experience in areas such as post-training and reinforcement learning for large models, reward design, large-scale distributed training, or evaluation of model behavior. * You are experienced in designing experiments and working with real-world datasets to validate research ideas. * You care about advancing understanding of user behavior and improving experiences across music and talk content. * You bring curiosity, creativity, and a thoughtful approach to solving complex, open-ended problems. * You value collaboration and actively seek diverse perspectives in your work. ### Where You’ll Be * This role is based in London. * 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. ### Compensation ### Additional Information 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. Show more [Apply now >](https://jobicy.com/jobs/155216-research-scientist-personalization.md) > Annual salary information is not provided for this position. 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