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HomeCompaniesSpotifyStaff Machine Learning Engineer - Content Intelligence

Staff Machine Learning Engineer - Content Intelligence

Spotify · New York, NY · Hybrid · Active · Lever

Job facts

FieldValue
CompanySpotify
TitleStaff Machine Learning Engineer - Content Intelligence
Normalized title-
Department / teamEngineering / Experience
LocationNew York, NY, United States
Work modelHybrid / Hybrid
Employment typePermanent
Salary-
Statusactive
ATS providerLever
Posted / first seen2026-05-05 / 2026-05-29
Changed / last seen2026-05-29 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Spotify.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Lever.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in New York.Open
Department jobsActive postings in Engineering.Open
Work model jobsActive Hybrid postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanySpotify
Source8f76458c-d40f-4324-bb14-bb757d1b7058
ATS providerLever

Description

We design Spotify’s consumer experience - end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. The Content Platform team powers the full lifecycle of content across music, podcasts, audiobooks, and emerging formats at Spotify. We ensure that everything from licensed catalog to user-generated content is trusted, safe, and high quality for millions of listeners worldwide. Our systems are responsible for how content is ingested, understood, enriched, governed, and distributed across the platform. As the scale and diversity of content continues to grow—driven by advances in AI and new creation tools—we’re building intelligent systems that can evaluate, manage, and route content reliably at global scale. We’re seeking a Staff Machine Learning Engineer to build and scale foundational ML systems that power content understanding across Spotify. In this role, you’ll work on systems that generate deep, machine-readable understanding of content across audio, video, text, and images—enabling automation, improving quality, and unlocking new product experiences. This work is central to delivering safe, high-quality, and differentiated experiences for millions of listeners and creators worldwide. The United States base range for this position is 227,495–324,993 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, 13 paid flexible holidays, and paid sick leave. These ranges may be modified in the future. What You Will Do Build and scale machine learning systems that generate deep understanding of content across modalities Develop models for classification, tagging, semantic understanding, and content enrichment Create high quality content enrichment at scale using LLMs and agentic systems. Design systems that make content intelligence signals available to downstream teams and products Improve automation for content quality, safety, and metadata enrichment at scale Collaborate with product, policy, and engineering teams to translate content intelligence into user impact Contribute to evaluation frameworks, data pipelines, and annotation systems Support rapid experimentation to prototype and launch new types of content signals Help improve system reliability, scalability, and performance across large datasets Who You Are You have experience building and deploying machine learning systems in production You are comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar You have experience working with large datasets and care about data quality and evaluation You are interested in or have worked with multimodal machine learning You understand how to design systems that balance automation with quality and user experience You are comfortable working on complex problems with evolving requirements You think in systems and understand how models connect to product outcomes You communicate clearly and work well across technical and non-technical teams Where You Will Be This role is based in New York, NY 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.

Full job record

Job ID76a82054d2c45c7b4c4f9ca9915c4b03b7a5b303
Org ID72fe3b06-0d08-4f7d-9dfd-beedeeda0a25
Source ID8f76458c-d40f-4324-bb14-bb757d1b7058
Board ID8f76458c-d40f-4324-bb14-bb757d1b7058
Providerlever
Provider Job Key76458bae-8a16-4c42-8780-f9452206f0e0
TitleStaff Machine Learning Engineer - Content Intelligence
Normalized Title
Statusactive
Activeyes
Location TextNew York, NY
DepartmentEngineering
TeamExperience
Employment TypePermanent
Workplace Typehybrid
Remote Policyhybrid
CountryUnited States
RegionNY
CityNew York
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://jobs.lever.co/spotify/76458bae-8a16-4c42-8780-f9452206f0e0
Apply URLhttps://jobs.lever.co/spotify/76458bae-8a16-4c42-8780-f9452206f0e0/apply
First Seen At2026-05-29 07:00:52Z
Last Seen At2026-06-06 07:56:15Z
Last Checked At2026-06-06 07:56:15Z
Last Changed At2026-05-29 07:00:52Z
Inactive At
Source Posted At2026-05-05 15:01:24Z
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=lever/board=spotify/date=2026-06-06/2026-06-06T07-56-15-191Z-c1c6a12102ce2af96a610c7ff3af0aa24b6d805515e5424bebb316f7d5eab721.json
Event Fields
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Parsed Structured
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Extensions
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Native Structured
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