Machine Learning Engineering Manager - LLM Serving & Infrastructure at Spotify - New York, NY

Team: Personalization

The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music and podcasts better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search, original playlists like Discover Weekly and Daylist, and are at the forefront of new innovations like AI DJ and AI Playlists.

Generative AI is transforming Spotify’s product capabilities and technical architecture. Generative recommender systems, agent frameworks, and LLMs present huge opportunities for our products to serve more user needs and use cases and unlock richer understanding of our content and users. This ML Manager will focus on the serving of a Unified Recommender model, based on open-weight LLM and transformer technology. You will collaborate with a diverse team to establish and implement the machine learning plan for the product domain, developing innovative recommendations and agent interactions. You will work as a technology leader, managing a team and influencing peers. You will collaborate with internal customers and platform teams, offering the opportunity to profoundly build the direction of the entire Spotify experience.

Join us and you’ll keep millions of users listening and engaging with our platform every day!

What You’ll Do

Lead a high-performing engineering team to develop, build, and deploy a high-scale, low-latency LLM Serving Infrastructure.

Drive the implementation of a unified serving layer to support multiple LLM models and inference types (batch, offline eval flows and real-time/streaming).

Lead all aspects of the development of the Model Registry for deploying, versioning, and running LLMs across production environments.

Ensure successful integration with the core Personalization and Recommendation systems to deliver LLM-powered features.

Define and champion standardized technical interfaces and protocols for efficient model deployment and scaling.

Establish and monitor the serving infrastructure's performance, cost, and reliability, including load balancing, autoscaling, and failure recovery.

Collaborate closely with data science, machine learning research, and feature teams (Autoplay, Home, Search, etc.) to drive the active adoption of the serving infrastructure.

Scale up the serving architecture to handle hundreds of millions of users and high-volume inference requests for internal domain-specific LLMs.

Drive Latency and Cost Optimization: partner with SRE and ML teams to implement techniques like quantization, pruning, and efficient batching to minimize serving latency and cloud compute costs.

Develop Observability and Monitoring: build dashboards and alerting for service health, tracing, A/B test traffic, and latency trends to ensure consistency to defined SLAs.

Contribute to Core LPM Serving: focus on the technical strategy for deploying and maintaining the core Large Personalization Model (LPM).

Who You Are

5+ years of experience in software or machine learning engineering, with 2+ years of experience managing an engineering team.

Hands-on with ML Engineering: you have deep expertise in building, scaling, and governing high-quality ML systems and datasets, including defining data schemas, handling data lineage, and implementing data validation pipelines (e.g., HuggingFace datasets library or similar internal systems).

Deep technical background in building and operating large-scale, high-velocity Machine Learning/MLOps infrastructure, ideally for personalization, recommendation, or Large Language Models (LLMs).

Proven track record to drive complex projects involving multiple partners and federated contribution models ("one source of truth, many contributors").

Expertise in designing robust, loosely coupled systems with clean APIs and clear separation of concerns (e.g., distinguishing between fast dev-time tools and rigorous production-like systems).

Experience integrating evaluation and testing into continuous integration/continuous deployment (CI/CD) pipelines to enable rapid 'fork-evaluate-merge' developer workflows.

Solid understanding of experiment tracking and results visualization platforms (e.g., MLFlow, custom UIs).

A pragmatic leader who can balance the need for speed with progressive rigor and production fidelity.

Where You’ll Be

This role is based in New York or Boston.

We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

Machine Learning Engineering Manager - LLM Serving & Infrastructure

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