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Spotify

CoLM 2026 — Intern

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

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Remote from
UK, USA, Sweden
Salary
Undisclosed
Department
Data Science & Analytics
Employment
Internship
Experience
Entry-Level, Junior
Published
Apply before
1 Nov 2026
Listing views
34
Application actions
0
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AI Summary

The role, at a glance.

This is a conference-specific expression-of-interest posting for students seeking machine learning and AI internships at Spotify, rather than a vacancy on a defined team. Potential work may span recommendation systems, search and ranking, natural language understanding, generative AI, and audio analysis. Candidates must have participated in or engaged with Spotify at the CoLM conference to be considered through this posting. Team placement and exact responsibilities will be determined later based on the candidate's background, interests, and experience.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

3/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role is technically demanding because Spotify's ML products operate at very large scale and may involve advanced modeling, ranking, language, or audio problems. As an internship pathway, candidates should expect structured mentorship while still being able to learn quickly and contribute independently.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive
$70,000
US market range$55k–$85k
AI insightNo actual compensation is disclosed in the posting. The figures are estimated US-market annualized pay for a machine learning/AI intern at a large technology company; actual pay may vary substantially by location, internship duration, academic level, and team.

Core skills

Skills and capabilities most closely associated with this opportunity.

Machine LearningArtificial IntelligenceRecommendation SystemsSearch RankingNatural Language ProcessingGenerative AIAudio AnalysisPythonData AnalysisExperimentation
Sample interview questions
Describe a machine learning project you built and explain how you evaluated whether it worked.

I would outline the problem, dataset, feature or model choices, and validation approach. I would discuss metrics tied to the objective, such as precision/recall, NDCG, or calibration, compare against a baseline, and explain what I learned from error analysis.

How would you approach improving recommendations for a listener with limited interaction history?

I would combine available contextual and content signals with collaborative information, using approaches such as content embeddings, popularity priors, and exploration. I would evaluate cold-start performance separately and use online experimentation to balance relevance, discovery, and long-term engagement.

What are key considerations when taking an ML model from an experiment into a production product?

I would consider data quality, reproducible feature pipelines, inference latency, scalability, monitoring, privacy, fairness, and model drift. I would also define rollback plans and success metrics before launch so that production behavior can be compared with the experimental baseline.

Explain how you would investigate a model whose offline metrics improved but whose online experiment did not.

I would first verify experiment integrity and metric definitions, then inspect whether the offline dataset or objective differs from real user behavior. I would segment results, review latency and serving issues, analyze novelty or diversity effects, and use qualitative error analysis to identify the mismatch.

How do you communicate a technical finding to a partner who does not specialize in machine learning?

I start with the user or business question, state the conclusion in plain language, and use a small number of meaningful metrics or visuals. I explain assumptions, uncertainty, trade-offs, and the recommended next step without relying on unnecessary technical jargon.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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 page to explore current openings.

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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 students who want to work on real problems at real scale as part of our internship program.

By applying here, you’re expressing interest in ML/AI internship 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.

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