Machine Learning Engineering Manager – Personalization

🕒 April 24

🗽 New York, Massachusetts – Remote

🗽 New York – Remote 🍂 Massachusetts – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👮‍♀️ Software Engineering Manager

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Spotify

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2008

📱 Media

👥 B2C

🛍️ eCommerce

Media • B2C • eCommerce

Spotify is a global audio streaming service company that offers its users access to a vast library of music and podcasts. Known for its focus on delivering personalized listening experiences, Spotify employs data scientists, engineers, and client partners across various global locations to continuously innovate and enhance its offerings. The company fosters a strong culture of diversity, equity, and impact, emphasizing belonging and inclusion in its workforce. Spotify is not only committed to shaping the future of audio entertainment but also to making a positive societal impact, with initiatives focused on social and environmental issues.

📋 Description

• Design, build, and improve machine learning systems that power safety across personalization surfaces such as recommendations, search, and emerging AI experiences • Contribute to the platformization of safety systems, enabling scalable and reusable solutions across teams • Develop and operate high-throughput, low-latency backend services powered by ML models • Partner with Product, Trust & Safety, and Content Platform to translate safety needs into practical technical solutions • Work on both traditional ML models and generative AI systems, including integrating third-party and in-house foundational models • Contribute to evaluation frameworks, including labeling strategies, ground truth creation, and model validation approaches • Collaborate with foundational model teams to embed safety into LLM-based and agent-driven experiences • Use metrics and experimentation to continuously improve system performance, safety outcomes, and user experience

🎯 Requirements

• You are experienced in building and deploying machine learning systems in production environments • You have hands-on experience with both traditional ML approaches and newer generative AI techniques • You have worked with scalable backend systems that require reliability, low latency, and high availability • You understand how to apply ML solutions to real-world product challenges, ideally in consumer-facing products • You have experience with model evaluation approaches such as labeling workflows, red-teaming, or ground truth data generation • You are comfortable working across disciplines, collaborating with product managers, researchers, and policy partners • You care deeply about building safe, responsible, and inclusive user experiences • You bring a thoughtful, metrics-driven approach to problem solving and decision-making

🏖️ Benefits

• Flexible work arrangements