At Spotify, the Artist-First AI Music lab is quietly pushing the boundaries of how technology can enhance music creation. Join the team that's not only building tools but is deeply invested in supporting artists and their unique visions through innovative AI applications.

Requirements

Proven experience fine-tuning large machine learning models with frameworks like PyTorch.
Familiarity with cloud platforms such as Google Cloud or AWS, enabling seamless model deployment.
Practical knowledge of debugging and optimizing training workflows to enhance efficiency across multiple compute resources.
Ability to effectively communicate across diverse global teams, balancing synchronous and asynchronous work styles.
Solid understanding of computer science fundamentals, including parallelism and thread safety.
Resourceful problem-solver with a proactive approach to overcoming obstacles in the development process.
Interest in audio processing and music information retrieval, with a desire to integrate these into your engineering work.

Benefits

Base salary ranges from $215,136 to $307,337, with additional equity options.
Comprehensive health insurance coverage to support your well-being.
Six months of paid parental leave, recognizing the importance of family.
401(k) retirement plan to help you plan for the future.
Monthly meal allowance to keep your energy up during those long coding sessions.
A generous leave policy with 23 paid days off and 13 flexible holidays.

You’ll Be a Great Fit If...

Your approach to problems is shaped by a collaborative spirit, always eager to share insights and learn from others. You thrive on challenges and find satisfaction in refining complex systems to achieve optimal performance. When encountering a technical hurdle, your first instinct is to explore, research, and dig into the details rather than waiting for direction. Your passion for music and technology intertwines, motivating you to contribute to projects that genuinely enhance the artist-fan relationship. You relish the journey of turning abstract research into tangible, functional models that users can interact with and enjoy.

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