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Spotify is seeking a Senior Machine Learning Engineer focused on personalization to join their innovative team. This role is designed for experienced professionals who are passionate about machine learning and its application in real-world music understanding and user experience. The role involves designing, building, and evaluating LLM (Large Language Model)-based solutions that enhance users' control over their music consumption, making the listening experience more adaptive and intuitive.
The primary duties of the Senior Machine Learning Engineer include:
Designing and implementing LLM-based solutions aimed at giving users enhanced control over their listening experience.
Collaborating on the development of prompted playlist experiences, concentrating on music fulfillment and session generation.
Working closely with cross-functional teams across product, design, user research, and data science to develop impactful features used by millions of listeners worldwide.
Prototyping innovative machine learning approaches and transitioning them into scalable production environments.
Building and refining systems that connect artists with their fans in personalized ways and contribute to the development of scalable ML systems serving large user bases.
Promoting best practices in ML system design, testing, evaluation, and deployment across Spotify’s organization.
The ideal candidate should possess:
A strong background in machine learning, natural language processing, and generative AI.
Experience in building and deploying end-to-end machine learning systems at scale.
Familiarity with LLM-based systems and techniques for their enhancement using human feedback, such as reinforcement fine-tuning.
Proficiency in designing modular ML architectures and writing technical specifications collaboratively with product teams.
Hands-on experience with distributed data processing tools like Apache Beam or Apache Spark.
Background working with cloud platforms such as Google Cloud Platform (GCP) or Amazon Web Services (AWS).
The ability to solve complex, real-world problems in a collaborative environment.
The role is primarily based in New York but offers flexibility to work from anywhere, which aligns with Spotify’s commitment to a work-life balance. While some in-person meetings are expected, working from home is generally supported.
The salary range for this position in the United States is $184,050 - $262,928, complemented by equity offerings. In addition to competitive pay, Spotify provides a comprehensive benefits package that includes:
Health insurance
Six months of paid parental leave
401(k) retirement plan
Monthly meal allowance
23 days of paid time off
13 paid flexible holidays
Sick leave
It's important to note that salary ranges may be adjusted in the future.
Spotify is committed to diversity and inclusivity in its workplace. They celebrate individuality, encouraging applicants from all backgrounds to apply. The organization believes that diverse perspectives and experiences enhance their culture and the quality of their products.
Additionally, Spotify strives to ensure that its recruitment process is accessible. They offer support and accommodations to applicants who may need assistance during the application or interview process.
Founded in 2008, Spotify has transformed the way people experience music. The company’s mission focuses on unlocking human creativity by providing artists with opportunities while giving fans access to a vast range of artistic content. They are recognized as the leading audio streaming subscription service.
For those looking to make an impact in the field of machine learning and contribute to the ever-evolving world of music, this position at Spotify presents a unique opportunity. The blend of work flexibility, a competitive salary, and a commitment to diversity makes it an attractive proposition for skilled candidates ready to innovate.
This job offer was originally published on weworkremotely.com
This job offer summary has been generated using automated technology. While we strive for accuracy, it may not always fully capture the nuances and details of the original job posting. We recommend reviewing the complete job listing before making any decisions or applications.
