Senior ML Engineer, Personalization & Search (Remote)

Alfie; United States; Hybrid; USD 184,000 - 263,000; 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

Paid flexible holidays

Paid sick leave

Job summary

Spotify is seeking a Senior Machine Learning Engineer for the Personalization team. You will build and optimize ML models that make Search more relevant and engaging, and develop ranking and retrieval systems to help users discover music, videos, and content.

You’ll explore agentic AI approaches, design experiments, and collaborate with cross-functional teams to ship improvements at scale. The role focuses on production-grade ML in a fast-moving environment, balancing experimentation with

Qualifications

• Proven experience building ML models for production systems.
• Strong understanding of information retrieval, search ranking, or personalization.
• Proficient in Python and modern ML frameworks.

Responsibilities

• Build and improve ML models to enhance Spotify Search relevance and personalization.
• Develop and optimize ranking and backend retrieval systems for content discovery.
• Explore new AI approaches to create intuitive search experiences.
• Design, implement, and evaluate ML solutions from experimentation to production.
• Collaborate with engineers, PMs, designers, and researchers to implement improvements.
• Use experiments and metrics to understand model behavior and guide product decisions.
• Contribute to scalable ML infrastructure and engineering practices.

Skills

Python

Machine Learning

Information Retrieval

Experimentation

Data Analysis

Backend Engineering

Tools

TensorFlow

PyTorch

Job description

Spotify is seeking a Senior Machine Learning Engineer for the Personalization team. You will build and optimize ML models that make Search more relevant and engaging, and develop ranking and retrieval systems to help users discover music, videos, and content.

You’ll explore agentic AI approaches, design experiments, and collaborate with cross-functional teams to ship improvements at scale. The role focuses on production-grade ML in a fast-moving environment, balancing experimentation with