Remote ML Engineer – Personalization & Recommendations

Spotify; New York (NY); Remote; USD 148,000 - 212,000; 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 Machine Learning Engineer for the Personalization team to advance home recommendations and improve user satisfaction. You will design, scale, and productionize ML models that power features like Discover Weekly and Made-for-you experiences.

You should be proficient with Python and Java/Scala, and have hands-on experience with PyTorch, Ray, and Hugging Face. Familiarity with Spark/Beam and cloud platforms (AWS/GCP) is preferred.

Qualifications

• Strong background in machine learning, statistics, and optimization.
• Experience building and deploying ML systems at scale.
• Hands-on with PyTorch, Ray, and Hugging Face.
• Familiarity with Spark/Beam and cloud platforms (AWS/GCP).
• Collaborates effectively with research, data science, and engineering teams.

Responsibilities

• Design, scale/build, evaluate, integrate, ship, and refine reward signals for recommendations.
• Lead collaborations and align across Personalization to integrate and A/B test mid-term signals in various recommendation systems.
• Promote and model best practices of ML systems development and testing across the organization.

Skills

Machine learning

Statistics

Optimization

Production ML systems

Python

Java

Scala

PyTorch

Ray

Hugging Face

Transformers

LLMs

Tools

Apache Spark

Apache Beam

Scio

AWS

GCP

Job description

Spotify is seeking a Machine Learning Engineer for the Personalization team to advance home recommendations and improve user satisfaction. You will design, scale, and productionize ML models that power features like Discover Weekly and Made-for-you experiences.

You should be proficient with Python and Java/Scala, and have hands-on experience with PyTorch, Ray, and Hugging Face. Familiarity with Spark/Beam and cloud platforms (AWS/GCP) is preferred.