[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) # Senior Machine Learning Engineer, 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[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 184,049–262,928 / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished1 Nov 2026Apply before1 Dec 2026Listing views49Application actions4Application 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 hiring a Senior Machine Learning Engineer to improve personalized Search and discovery experiences across music, video, and audiobooks. The role builds production machine-learning models, ranking systems, and backend retrieval capabilities at large scale. It combines experimentation, data analysis, and product collaboration to take ideas from early validation through reliable deployment. The engineer will also explore LLM and agentic approaches for conversational Search while contributing to scalable ML infrastructure. This is a hybrid US role aligned to Eastern Standard Time with some required in-person meetings. ## 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 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior-level production ML role requiring depth in search ranking, retrieval, personalization, experimentation, and scalable backend systems. The candidate must make sound technical decisions amid ambiguity while delivering measurable product impact to a large user base. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$223,488.50US market range (per year)$180k–$290k0$319k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a US base-pay range of $184,049–$262,928 USD. The yearly cadence is inferred from the compensation level and the senior, full-time US machine-learning engineering context. A reasonable estimated US market range for a senior ML/search engineer is $180,000–$290,000 USD per year; equity may add meaningful total compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine learning](https://jobicy.com/jobs?search_keywords=Machine%20learning.md)[Information retrieval](https://jobicy.com/jobs?search_keywords=Information%20retrieval.md)[Search ranking](https://jobicy.com/jobs?search_keywords=Search%20ranking.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md)[Recommendation systems](https://jobicy.com/jobs?search_keywords=Recommendation%20systems.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Backend engineering](https://jobicy.com/jobs?search_keywords=Backend%20engineering.md)[Large-scale data processing](https://jobicy.com/jobs?search_keywords=Large-scale%20data%20processing.md)[Experiment design](https://jobicy.com/jobs?search_keywords=Experiment%20design.md)[A/B testing](https://jobicy.com/jobs?search_keywords=AB%20testing.md)[Model evaluation](https://jobicy.com/jobs?search_keywords=Model%20evaluation.md)[Machine learning infrastructure](https://jobicy.com/jobs?search_keywords=Machine%20learning%20infrastructure.md)[Large language models](https://jobicy.com/jobs?search_keywords=Large%20language%20models.md)[Agentic AI](https://jobicy.com/jobs?search_keywords=Agentic%20AI.md)[Cross-functional collaboration](https://jobicy.com/jobs?search_keywords=Cross-functional%20collaboration.md) Sample interview questionsHow would you approach improving relevance for a personalized music search ranking system?I would first define the target user outcomes and offline and online metrics, such as successful plays, skips, reformulations, and long-term engagement. I would inspect query, listener, and content features; establish strong retrieval and ranking baselines; then test improvements through offline evaluation and carefully designed online experiments. I would also segment results to ensure gains are robust across query types and listener cohorts. Describe how you would design a retrieval and ranking architecture for Spotify-scale Search. I would use a multi-stage design: candidate generation optimized for recall, followed by a more expressive ranking model optimized for relevance and engagement. Candidate sources could include lexical retrieval, embedding-based retrieval, collaborative signals, and popularity or freshness sources. I would monitor latency, coverage, bias, and feature availability so that the architecture remains reliable in production. How do you decide whether an experiment result should lead to a production launch? I validate instrumentation and sample quality first, then assess statistical confidence, practical effect size, guardrail metrics, and consistency across relevant segments. I look beyond a single proxy metric to potential effects on retention, content diversity, latency, and user trust. If results are uncertain, I would refine the hypothesis or run a targeted follow-up experiment rather than overgeneralize. What production challenges have you considered when deploying machine-learning models? Key concerns include training-serving skew, feature freshness, data quality, model latency, versioning, reproducibility, and safe rollback procedures. I would establish monitoring for input drift, prediction distributions, system reliability, and business metrics. A staged rollout with holdouts and clear alert thresholds helps reduce risk while validating real-world performance. Where could LLMs or agentic systems add value to conversational Search, and what risks would you manage? They can help interpret ambiguous natural-language intent, support multi-turn refinement, and generate structured search plans that call retrieval tools. I would constrain them with grounded retrieval, deterministic safety and policy layers, latency budgets, and evaluation sets representing real listener queries. I would also measure hallucination, relevance, privacy exposure, and the incremental value over conventional ranking approaches. 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. Growlithe is a fast-moving squad within Spotify’s Conversation Product Area, focused on bringing emerging Search and agentic experiences to life. We explore, define, build, validate, and optimize new ideas to help listeners discover the content they love. Our work spans personalized Search, music video discovery and ranking, audiobook Search expansion, and the foundations of more social Search experiences. We’re looking for a Senior Machine Learning Engineer to help shape the next generation of Search at Spotify. You’ll work at the intersection of machine learning, personalization, and product development, turning complex challenges into intuitive discovery experiences for millions of listeners. ### What You’ll Do * Build and improve machine learning models that make Spotify Search more relevant, personalized, and engaging for listeners. * Develop and optimize ranking and backend retrieval systems that help listeners discover music, videos, audiobooks, and other content. * Explore emerging AI and agentic approaches to create more intuitive and conversational Search experiences. * Design, implement, and evaluate machine learning solutions, taking ideas from early experimentation through production. * Partner with engineers, product managers, designers, and researchers to identify opportunities and deliver meaningful improvements to Search. * Use experimentation, data analysis, and performance metrics to understand model behavior, validate hypotheses, and improve the listener experience. * Contribute to scalable machine learning infrastructure and engineering practices that support reliable, high-quality Search experiences. * Share knowledge, challenge assumptions constructively, and help shape the technical direction of machine learning within the squad. ### Who You Are * You have experience building, deploying, and improving machine learning systems in production environments. * You have a strong understanding of machine learning approaches for information retrieval, search ranking, recommendation systems, or personalization. * You are proficient in Python and comfortable working with modern machine learning frameworks and data processing tools. * You have experience and a desire to lean into backend engineering at Spotify scale. * You understand how to design and evaluate experiments, interpret model performance, and translate technical findings into product improvements. * You have experience working with large-scale datasets and understand the challenges of building reliable, scalable machine learning systems. * You are curious about emerging AI technologies, including large language models and agentic systems, and interested in their potential to improve discovery experiences. * You enjoy working across disciplines, communicating technical ideas clearly, and learning from different perspectives. * You are comfortable navigating ambiguity, taking ownership of complex challenges, and balancing experimentation with practical delivery. ### Where You’ll Be * This role is based in Eastern Standard Time * 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 The United States base range for this position is 184,049–262,928 USD, 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, paid flexible holidays, and 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. Show more [Apply now >](https://jobicy.com/jobs/158389-senior-machine-learning-engineer-personalization.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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