Description:

The Staff Machine Learning Engineer will be part of the Content and Catalog Management (CoCaM) team at Spotify, which is central to the ingestion, distribution, management, and growth of all content on the platform.
This role involves owning the ML strategy for content and catalog management across six squads, ensuring the platform supports diverse content types and enables fast and accurate decision-making.
The engineer will enhance ML competence among over 40 engineers and product managers, providing mentorship on integrating ML with traditional engineering approaches.
Collaboration with Staff Engineers, Product partners, and operational teams is essential to identify and demonstrate ML opportunities, fostering critical thinking within teams.
The role promotes a culture of collaboration, openness, and inclusion to develop well-rounded solutions and encourages experimentation and iteration in engineering problem-solving.
The engineer will drive technical decisions and best practices in ML to ensure high-quality and scalable solutions are built.
Staying updated with the latest ML advancements and developing a learning environment for continuous improvement of the team's skills is a key responsibility.

Requirements:

A proven track record of creating and growing ML strategies for platforms, delivering horizontal solutions to vertical problems is required.
Hands-on experience in implementing ML systems at scale using Java, Scala, Python, or similar languages, along with ML-specific libraries and frameworks like TensorFlow or PyTorch is necessary.
In-depth knowledge of various ML algorithms, including supervised, unsupervised, and reinforcement learning, with experience in algorithm selection, tuning, and evaluation is essential.
The candidate must have experience communicating complex ML practices and solutions to both technical and non-technical audiences, with a focus on education and understanding.
Experience in leading ML projects, mentoring less experienced engineers, and driving technical strategy within teams is required.
Familiarity with containerization and orchestration tools such as Docker and Kubernetes is necessary.
Experience with cloud platforms like GCP, AWS, or Microsoft Azure, and familiarity with cloud-based ML services and tools is essential.
The candidate should be comfortable writing queries, exploring data, and collaborating on hypotheses with product and engineering counterparts.
Knowledge of model deployment techniques and serving frameworks like TensorFlow Serving or custom APIs is required.

Spotify offers a distributed workforce model, allowing employees to choose a work mode that suits them best, with a preference for candidates in Stockholm, London, or Dublin.
The company provides various options for working preferences, including the ability to work from home or in an office.
Spotify is committed to being an equal opportunity employer, welcoming individuals from diverse backgrounds and experiences.
Employees are encouraged to bring their personal experiences and perspectives to foster a thriving and innovative workplace.