Machine Learning Engineer - Platform

The Hendrix Serving team is dedicated to developing a robust, Spotify-wide platform for serving machine learning models. This platform streamlines the productization of ML models by mitigating the incidental complexities involved in crafting backend services for serving predictions.

What You'll Do

• Collaborate with other Machine Learning Engineers, Researchers, and various Product teams to deliver scalable inference services that meet the timelines and specifications of given requirements
• Work independently and collaboratively on squad projects that often requires learning and applying new technologies that may go beyond existing skills
• Design, document and implement reliable, testable and maintainable solutions ML infrastructure capabilities

Who You Are

• You have 3+ years of hands-on experience implementing production machine learning infrastructure at scale in Python, Java, or similar languages
• You have a general understanding of data pipelines
• You have experience with tools like TensorFlow or PyTorch
• You are familiar with agile software processes and modular code design following industry standards

Where You'll Be

• Hybrid role based in our New York office (average 2 to 3 days a week required)
• Working hours? We operate within the Eastern time zone for collaboration

Our global benefits

Extensive learning opportunities, through our dedicated team, GreenHouse.

Flexible share incentives letting you choose how you share in our success.

Global parental leave, six months off - fully paid - for all new parents.

All The Feels, our employee assistance program and self-care hub.

Flexible public holidays, swap days off according to your values and beliefs.

Learn about life at Spotify

The United States base range for this position is $136, - $195, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance.