Senior ML Engineer, Personalization & Discovery

Spotify; Boston (MA); Remote; USD 227,000 - 325,000; Full time

Benefits offered by this job

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

Job summary

Spotify is seeking a Staff Machine Learning Engineer to shape the future of personalized discovery at scale. You will work on recommendation systems and large-language models, bringing research into production and setting technical direction.

You will own models for Home feed, train and optimize LLMs, and collaborate across product, engineering, and data science teams to run experiments and improve user experiences at global scale.

Qualifications

• Deep experience building and deploying ML systems in production.
• Experience with recommendation or large-scale content discovery.
• Proficient in Python and PyTorch.
• Experience with LLM training, fine-tuning, and optimization (SFT, distillation, LoRA).
• Experience with large-scale inference, latency, reliability, and cost concerns.
• Strong collaboration and communication with partners.

Responsibilities

• Own and improve ML models powering the Home feed and Shortcuts.
• Design, build, and ship personalized recommendations for millions of Spotify listeners.
• Train, fine-tune, evaluate, and optimize large language models (SFT, distillation, LoRA).
• Collaborate with PMs, engineers, data scientists, and designers on experiments.
• Improve ML platform capabilities, data pipelines, and production systems.
• Mentor fellow ML engineers and share best practices.

Skills

Python

PyTorch

LLMs

A/B testing

Experimentation

Distributed ML

Communication

Tools

Python

PyTorch

Ray

Job description

Spotify is seeking a Staff Machine Learning Engineer to shape the future of personalized discovery at scale. You will work on recommendation systems and large-language models, bringing research into production and setting technical direction.

You will own models for Home feed, train and optimize LLMs, and collaborate across product, engineering, and data science teams to run experiments and improve user experiences at global scale.