Staff Machine Learning Engineer, Personalization at Spotify
New York, NY
$227,495–$324,993from the description
Jul 29, 2026
New York, NY
Sep 23, 2026
What this job asks for AI summary
A Staff Machine Learning Engineer role on Spotify's Personalization team, focused on building and owning the ML models and systems behind the Home feed and Shortcuts experience. The work spans recommendation systems, large language model fine-tuning, and production-scale ML infrastructure serving millions of listeners globally. The role carries cross-team technical leadership responsibilities alongside hands-on model development and experimentation.
Staff level · 8+ years · Remote · Full-time
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“or” means any one of them counts — you don't need all of them.
Posted 3 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 1,050 people nationally plausibly meet what this posting asks for (data scientists). range 390–1,350
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 1, 2026. It is a model, not a headcount.
Why we read it this way (5)
The role sits at the boundary between Data Scientists (15-2051) and Software Developers (15-1252): it involves deep ML modeling, LLM fine-tuning, and research-to-production work (pointing to 15-2051), but also significant production engineering — data pipelines, inference systems, and platform work (pointing to 15-1252). 15-2051 was chosen as primary because the core deliverable is ML model quality and personalization intelligence, not general software systems.
The posting covers the entire North Americas region (remote-eligible), not a single metro. The CBSA shown reflects Spotify's US presence; the actual work location may vary across the US.
Distributed ML frameworks (Ray, FSDP, HSDP) and orchestration tools (Flyte, Airflow, BigQuery) appear in the 'Who You Are' requirements section but are introduced with 'such as' / 'technologies such as', indicating the specific tools are interchangeable. They are captured as preferred with alternatives accordingly.
LLM fine-tuning techniques (SFT, distillation, LoRA) are stated as required experience; SFT is captured under 'supervised fine-tuning' and distillation is folded into the LLM fine-tuning requirement rather than emitted as a standalone skill, as it is a technique rather than a named tool.
No minimum degree is stated anywhere in the posting.
Read the full posting
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