Staff ML Engineer - Global Personalization & Recommendations

Spotify; New York (NY); Remote; USD 227,000 - 325,000; Full time

Benefits offered by this job

Health insurance

Six month parental leave

401(k) retirement plan

Monthly meal allowance

23 paid days off

13 paid flexible holidays

Paid sick leave

Job summary

Spotify, a leader in audio streaming, seeks a Staff ML Engineer to shape personalized discovery at global scale. You will own production ML systems powering Home feeds and podcast recommendations, collaborating with PMs, engineers, data scientists, and designers to deliver impactful experiences.

You'll advance experiments, optimize large language models, and drive efficient inference across diverse surfaces, while mentoring peers and contributing to Spotify's evolving personalization platform.

Qualifications

• Experience building and deploying ML systems in production.
• Experience with recommendation systems or large-scale content discovery.
• Proficiency in Python and PyTorch.
• Experience with LLM training, fine-tuning, and optimization (SFT, distillation, LoRA).

Responsibilities

• Own and improve ML models and systems powering Home feed and Shortcuts.
• Design and ship personalized recommendations for millions of listeners.
• Build content recommendation systems for AI-powered user experiences.
• Train, fine-tune, and evaluate large language models using SFT, distillation, and LoRA.
• Drive experimentation strategies and optimize model quality, reliability, and cost.

Skills

Python

PyTorch

A/B testing

Distributed ML

Tools

Ray

Airflow

Flyte

BigQuery

Job description

Spotify, a leader in audio streaming, seeks a Staff ML Engineer to shape personalized discovery at global scale. You will own production ML systems powering Home feeds and podcast recommendations, collaborating with PMs, engineers, data scientists, and designers to deliver impactful experiences.

You'll advance experiments, optimize large language models, and drive efficient inference across diverse surfaces, while mentoring peers and contributing to Spotify's evolving personalization platform.