Spotify is hiring a
Staff Machine Learning Engineer

Summary

Join us in helping Spotify give its users what they want through the Content and Catalog Management team.

Requirements

• You have a proven track record of creating, promoting and growing ML strategy for platforms and have used it to deliver horizontal solutions to vertical problems
• You have hands-on experience in implementing ML systems at scale in Java, Scala, Python or similar and also with ML-specific libraries and frameworks like TensorFlow, PyTorch or similar
• You have in-depth knowledge of various ML algorithms, including supervised, unsupervised, and reinforcement learning, and have experience with algorithm selection, tuning, and evaluation
• You have deep experience communicating sophisticated ML practices, solutions and algorithms to technical and non-technical parties unfamiliar with ML terminology and principles
• You have shown experience in leading ML projects and mentoring more inexperienced engineers, and driving technical strategy and decision-making within teams
• You have experience with containerization and orchestration tools like Docker and Kubernetes
• You have experience with cloud platforms such as GCP, AWS, or Microsoft Azure, and familiarity with cloud-based ML services and tools, such as Google AI Platform, AWS SageMaker or Azure Machine Learning
• You are comfortable writing queries, exploring data, and collaborating on hypotheses with product and engineering counterpart
• You have knowledge of model deployment techniques and serving frameworks like TensorFlow Serving, TorchServe, or custom APIs

Responsibilities

• Own the ML strategy for content and catalog management across six squads
• Ensure the platform can support diverse content types, policies, provides data for reporting and auditability and enables the right balance of fast and accurate decisions
• Enhance the ML competence among 40+ engineers, engineering managers and product managers
• Partner with Staff Engineers and Product partners, policy owners and operational teams, to identify and demonstrate ML opportunities
• Cultivate strong relationships within the CoCaM Staff Engineers, Engineering Managers and Product Managers and promote the culture of collaboration, openness and inclusion to develop more well-rounded solutions
• Promote experimentation and iteration to encourage more out-of-the-box approaches to engineering problems that we face and balance the need to deliver on the outcomes and goals we’re committed to
• Drive technical decisions and standard methodologies in ML to ensure high-quality and scalable solutions are built by our engineers
• Stay updated with the latest ML advancements and Spotify ML standards and develop a learning environment to continuously improve the team's skills and knowledge