Staff ML Engineer at Spotify: LLMs & Rec Systems

Spotify; United States; Remoto; USD 227.000 - 325.000; Tempo pieno

Descrizione del lavoro

Spotify is seeking a Staff ML Engineer focused on LLMs and recommendation systems in Wilmington. This senior, high-impact role requires ownership of system architecture, mentorship, and broad technical influence across teams.

The position offers top-market compensation with base plus equity, reflecting seniority and market demand in the US. Expect responsibilities spanning LLM fine-tuning, large-scale recommendations, and advanced MLOps workflows.

Competenze

• 8+ years of experience in ML engineering or similar senior roles.
• Strong background in large language models and recommendation systems.
• Experience with distributed training and MLOps pipelines.

Mansioni

• Own system architecture for LLM and recommendation workloads.
• Mentor cross-functional teams and influence multiple squads.
• Lead research-to-prod efforts on LLM fine-tuning, deployment, and UX patterns.

Conoscenze

LLM fine-tuning

Production-scale recommender systems

Agentic AI & AI-powered UX

Distributed ML training & MLOps

Strumenti

Ray

FSDP

Descrizione del lavoro

Staff ML Engineer at Spotify: LLMs & Rec Systems in Wilmington

Senior-Level, High-Impact Role: The Staff ML Engineer title at Spotify sits above Senior Engineer and carries cross-team technical influence. With 8+ years required, this is a principal-track position - expect ownership of system architecture and mentorship responsibilities, not just individual contribution.

Compensation vs. Market: At $227K - $325K base plus equity, this is at the top quartile for Staff ML Engineers in the US market (Levels.fyi data shows Staff ML Engineers at large tech companies averaging $250K - $350K total comp). The equity component could significantly increase total compensation.

High-Demand Skills You'll Build:

• LLM fine-tuning (SFT, LoRA, distillation) - among the most sought-after skills in 2024 - 2025
• Production-scale recommendation systems at hundreds of millions of users
• Agentic AI and emerging AI-powered UX patterns
• Distributed ML training (Ray, FSDP) and MLOps pipelines

Green Flags: Spotify's Personalization team has shipped proven, beloved products (Discover Weekly, Blend), signaling a mature engineering culture with real user impact. The mention of mentorship and cross-functional collaboration suggests a collaborative, not siloed, environment.

Potential Considerations: The role spans a very broad technical surface (LLMs + recommendation systems + MLOps), which may mean context-switching. Candidates should clarify team size and whether the Staff role has a defined technical lead vs. IC path before applying.

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