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HomeCompaniesSpotifyStaff Machine Learning Engineer

Staff Machine Learning Engineer

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

Job facts

FieldValue
CompanySpotify
TitleStaff Machine Learning Engineer
Normalized title-
Department / teamEngineering / Personalization
LocationNew York, NY, United States
Work modelRemote / Remote
Employment typePermanent
Salary-
Statusactive
ATS providerLever
Posted / first seen2026-04-01 / 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 Remote 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

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. We are looking for a Machine Learning Engineer to join the Personalization team - an area of hardworking engineers that are passionate about understanding what drives user satisfaction with Spotify - and who make impactful changes to Home recommendation systems to achieve this goal. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction. The United States base range for this position is $227,495- $324,993 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, 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. What You'll Do Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization. Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems Who You Are You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes. You have hands-on experience with large cross-collaborative machine learning projects and managing stakeholders. You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required. You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS. You care about agile software processes, data-driven development, reliability, and disciplined experimentation. Where You'll Be We offer you the flexibility to work where you work best! For this role, you can be within the North America and EMEA region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration

Full job record

Job ID9dc10e774f74cc06ac05b62b7f552dbcb241e534
Org ID72fe3b06-0d08-4f7d-9dfd-beedeeda0a25
Source ID8f76458c-d40f-4324-bb14-bb757d1b7058
Board ID8f76458c-d40f-4324-bb14-bb757d1b7058
Providerlever
Provider Job Key736f1827-6b26-4b3b-b8d8-1d754296e033
TitleStaff Machine Learning Engineer
Normalized Title
Statusactive
Activeyes
Location TextNew York, NY
DepartmentEngineering
TeamPersonalization
Employment TypePermanent
Workplace Typeremote
Remote Policyremote
CountryUnited States
RegionNY
CityNew York
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://jobs.lever.co/spotify/736f1827-6b26-4b3b-b8d8-1d754296e033
Apply URLhttps://jobs.lever.co/spotify/736f1827-6b26-4b3b-b8d8-1d754296e033/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 At2026-04-01 14:44:17Z
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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  "last_changed_at": "2026-05-29T07:00:52.396Z",
  "active_status": "active"
}
Parsed Structured
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Extensions
{}
Native Structured
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    },
    {
      "text": "Where You'll Be",
      "content": "<div>\n<ul style=\"margin-top: 0px; margin-bottom: 0px; padding-inline-start: 48px;\">\n<li style=\"font-size: 12pt; font-family: 'DM Sans', sans-serif; color: #222326;\">\n<p style=\"background-color: #ffffff; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt;\">We offer you the flexibility to work where you work best! For this role, you can be within the North America and EMEA region as long as we have</span><a href=\"https://lifeatspotify.com/being-here/work-from-anywhere\" style=\"text-decoration: none;\"><span style=\"font-size: 12pt; color: #222326;\"> </span><span style=\"font-size: 12pt; color: #1155cc; text-decoration: underline; text-decoration-skip-ink: none;\">a work location.</span></a></p>\n</li>\n<li style=\"font-size: 12pt; font-family: 'DM Sans', sans-serif; color: #222326;\">\n<p style=\"background-color: #ffffff; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt;\">This team operates within the Eastern Standard time zone for collaboration</span></p>\n</li>\n\n</ul></div>"
    }
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  "updatedAt": null,
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    "location": "New York, NY",
    "commitment": "Permanent",
    "department": "Engineering",
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  },
  "salaryRange": null,
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}
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