<p> 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<br /> <br /> 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.<br /> <br /> What You'll Do<br /> <br /> • 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.<br /> • Develop novel methodologies, models, and evaluation frameworks to advance personalization systems.<br /> • Design and execute rigorous experiments to explore new ideas and validate research hypotheses.<br /> • Contribute to the scientific community through publications, talks, and conference participation.<br /> • Collaborate with cross-functional partners to translate research insights into long-term product opportunities.<br /> • Help define and evolve a forward-looking research agenda aligned with Spotify's personalization strategy.<br /> • Mentor others and contribute to a strong, curious, and collaborative research culture.<br /> <br /> Who You Are<br /> <br /> • You have a Master's or PhD in machine learning, AI, or a related field, or equivalent research experience.<br /> • You have demonstrated expertise in machine learning and artificial intelligence through peer-reviewed publications at conferences such as NeurIPS, ICLR, ICML, KDD, or RecSys.<br /> • 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.<br /> • You are experienced in designing experiments and working with real-world datasets to validate research ideas.<br /> • You care about advancing understanding of user behavior and improving experiences across music and talk content.<br /> • You bring curiosity, creativity, and a thoughtful approach to solving complex, open-ended problems.<br /> • You value collaboration and actively seek diverse perspectives in your work.<br /> <br /> Where You'll Be<br /> <br /> • This role is based in New York City.<br /> • 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.<br /> <br /> The United States base range for this position is $133,194 - $190,278 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, a monthly meal allowance, 23 paid days off, 13 paid flexible holidays. These ranges may be modified in the future.<br /> <br /> 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.<br /> <br /> We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Find our AI notice here: /> <p id='jobcode'>Jobcode: Reference SBJ-j6v451-139-5-253-10-42 in your application.</p> </p>