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Senior Machine Learning Infrastructure Engineer, Music

EXTRA HOLIDAYS - EXTRA PARENTAL LEAVE

Remote:

Hybrid

Contract:

Full time

Salary:

172 - 246K yearly

Experience:

Senior (5-10 years)

Work from:

New York (US)

Offer summary

Qualifications:

Professional experience in machine learning systems, Strong grasp of Python and willingness to work in other languages as necessary, Experience with cloud platforms like GCP, AWS, or Azure.

Key responsabilities:

Build data processing pipelines at petabyte-scale
Collaborate across teams to integrate audio ML research into products
Develop infrastructure and tooling to support model training
Debug problems across the stack and deploy ML models for production
Maintain Spotify-owned open-source audio software infrastructure

Spotify Music XLarge https://www.lifeatspotify.com/

5001 - 10000 Employees

HQ: Stockholm

See more Spotify offers

Job description

Your missions

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

We offer you the flexibility to work where you work best! For this role, you can be within the EST time zone as long as we have a work location.

The United States base range for this position is $171,903.00 - $245,575.00 plus equity. The benefits available for this position include 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. These ranges may be modified in the future.