[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) # CoLM 2026 — Full Time 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), [USA](https://jobicy.com/job-region/usa.md), [Sweden](https://jobicy.com/job-region/sweden.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceOpen levelPublished2 Oct 2026Apply before1 Nov 2026Listing views39Application actions2Application 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. This is a conference-specific talent pipeline posting for CoLM participants interested in future machine learning and AI opportunities at Spotify. It is not attached to a defined team, level, or job description; the recruiting team will assess fit based on each candidate’s background and interests. Potential work areas include recommendation systems, search and ranking, natural language understanding, generative AI, and audio analysis. Candidates should be prepared to demonstrate applied ML or AI expertise and the ability to work on large-scale consumer-product challenges. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 4/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightSpotify’s ML/AI work operates at substantial scale and may involve complex modeling, experimentation, ranking, language, or audio problems. Because this is a general talent-pool posting rather than a defined position, the precise seniority and technical depth will depend on the eventual team match. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$160,000US market range$120k–$220k0$242k AI insightNo actual salary is disclosed in the posting. These are estimated annual US-market base-salary figures for full-time machine learning, AI research, and applied ML engineering opportunities at a large technology company; actual compensation will vary significantly by level, specialization, location, and equity or bonus eligibility. ## 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)[recommendation systems](https://jobicy.com/jobs?search_keywords=recommendation%20systems.md)[search and ranking](https://jobicy.com/jobs?search_keywords=search%20and%20ranking.md)[natural language processing](https://jobicy.com/jobs?search_keywords=natural%20language%20processing.md)[generative AI](https://jobicy.com/jobs?search_keywords=generative%20AI.md)[audio analysis](https://jobicy.com/jobs?search_keywords=audio%20analysis.md)[data science](https://jobicy.com/jobs?search_keywords=data%20science.md)[experimentation](https://jobicy.com/jobs?search_keywords=experimentation.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md) Sample interview questionsDescribe an ML system you built or improved that had measurable user or business impact.I would outline the problem, data sources, model choice, offline and online evaluation approach, deployment process, and measurable outcome. I would also explain trade-offs, failure modes, and what I would improve in a subsequent iteration. How would you evaluate a recommendation or ranking model beyond offline accuracy metrics? I would combine offline measures such as NDCG, recall, calibration, and coverage with online experiments measuring engagement, retention, satisfaction proxies, diversity, and long-term user outcomes. I would also monitor fairness, novelty, and unintended feedback loops. What steps would you take to move a prototype generative-AI model into a production environment? I would define the product objective and safety requirements, establish evaluation datasets and guardrails, build scalable serving and monitoring, run controlled experiments, and create rollback procedures. I would continuously review quality, latency, cost, privacy, and user feedback after launch. Tell us about a time you worked with cross-functional partners to make a technical decision. I would explain how I translated technical alternatives into user impact, risk, timeline, and operational considerations. I would show how I incorporated input from product, engineering, design, and research partners, documented the decision, and aligned the group on success metrics. How do you investigate a model whose production performance has degraded? I would first validate instrumentation and compare production data with training and validation distributions. Then I would examine data quality, feature drift, label delays, serving changes, segment-level performance, and experiment exposure before deciding whether to retrain, recalibrate, revise features, or roll back. Opportunity details ## About this role. PLEASE READ BEFORE APPLYING This posting is intended specifically for candidates who are participating in CoLM and is not a general application for open roles at Spotify. When applying, you’ll be asked to select how you engaged with us at the conference. Please answer this question accurately. Applications indicating conference participation or engagement that did not occur may be removed from consideration. If you are interested in opportunities at Spotify but are not attending CoLM please visit our [Life at Spotify](https://www.lifeatspotify.com/jobs) page to explore current openings. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Thanks for connecting with the Spotify team at the conference! This posting is a way for us to stay in touch and keep the conversation going. At Spotify, machine learning and AI are at the heart of how we personalize music, podcasts, and audiobooks for hundreds of millions of listeners around the world. Our ML teams tackle problems across recommendation systems, search and ranking, natural language understanding, generative AI, audio analysis, and more — and we’re always looking for talented researchers and engineers who want to do impactful work at scale. By applying here, you’re expressing interest in ML/AI opportunities at Spotify. This isn’t tied to a specific role or team — our team will follow up to explore the right fit based on your background, interests, and experience. ### 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/154384-colm-2026-full-time.md) > Annual salary information is not provided for this position. 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