We are looking for a Machine Learning Infrastructure Engineer to help us build the next generation of AI-based audio understanding technologies. Our team expands the state of the art in AI-based machine listening technology, which enables new ways to search, re-use, explore, understand, and recommend music. We build products for Spotify users, artists, music labels, publishers, managers, advertisers, and more. Most of our projects cut across all of Spotify and have company-wide impact. We develop features to improve products and lay the foundations for exciting new product opportunities.
What You'll Do
Build massive data processing pipelines to run ML models on audio at petabyte-scale
Work cross-functionally with other engineers, research scientists, and product managers to build audio ML research into Spotify’s products to serve hundreds of millions of users
Build best-in-class infrastructure and tooling to support and accelerate model training
Work closely with research scientists to debug, optimize, and convert ML models for deployment
Debug problems across the entire stack, from low-level debugging to high-level system design
Collaborate with customer teams to deploy our research in products around Spotify, in backend, data, mobile, core, and other code bases
Scope the feasibility of projects through quick prototyping to assess performance, quality, time and cost
Help maintain crucial pieces of Spotify-owned open-source audio software infrastructure like Pedalboard, Basic Pitch, and more
Who You Are
You have professional experience working with machine learning systems
You have a very strong grasp of Python, but are happy to work in languages like Java, Scala, C++, and others as necessary
You have very strong communication skills and can collaborate effectively with ML researchers, product stakeholders, and executives across various geographies
You have strong systems fundamentals and can debug problems down to the operating system level
You have a good understanding of performance and can design and engineer systems that scale without breaking the bank
You have experience debugging, profiling, optimizing, or deploying ML models
You have worked with cloud platforms like GCP, AWS, or Azure
You have proven experience implementing and maintaining large-scale production software systems
You are interested in learning more about audio processing and music information retrieval and you're excited about building products that use such technologies
You are willing to go on call to support the systems we build, and are willing to build systems for reliability to avoid on-call fatigue. (The team averages 1 incident per quarter.)
Experience with deep learning techniques for content based processing (audio, image, video data) is a plus
Where You'll Be
This role will be located in our London or Stockholm office
