Staff Machine Learning Engineer

Who you are

• You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes
• You have hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
• You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required
• You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS
• You care about agile software processes, data-driven development, reliability, and disciplined experimentation

What the job involves

• The Personalization team makes deciding what to play next easier and more enjoyable for every listener
• From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features
• We built them by understanding the world of music and podcasts better than anyone else
• Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them
• We are looking for a Machine Learning Engineer to join the Personalization team - an area of hardworking engineers that are passionate about understanding what drives user satisfaction with Spotify - and who make impactful changes to Home recommendation systems to achieve this goal
• As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction
• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

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