Delivering the best Spotify experience possible. To as many people as possible. In as many moments as possible. That’s what the Experience team is all about. We use our deep understanding of consumer expectations to enrich the lives of millions of our users all over the world, bringing the music and audio they love to the devices, apps and platforms they use every day. Know what our users want? Join us and help Spotify give it to them.
The Content and Catalog Management (CoCaM) team works at the heart of the Content Platform R&D studio, the central point for the ingestion, distribution, management, knowledge and growth of all content you experience through Spotify products. In CoCaM, we drive the management of content and make decisions that impact the whole of Spotify on all contents appropriateness, availability, quality and accuracy. Through reactive and proactive reporting mechanisms we use the knowledge of Content Platform and apply platform & business policy with content, user, financial and experiential context to make and store a decision best for Creators, Consumers and Spotify.
We are seeking a Machine Learning (ML) Staff Engineer eager to own the definition, adoption and expansion of ML usage within our content and catalogue management platform. You’ll work with a community of engineers, researchers, product managers, designers and data scientists with varied levels of exposure and experience in ML. Together with the CoCaM Engineering Lead, Content Platform Engineering Leadership and fellow Staff Engineers; you’ll own the expansion of CoCaM ML knowledge and expertise, and collaborate to find opportunities for more efficient, effective and consistent use of ML in our decision-making pipeline.
What You'll Do
- 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. Provide mentorship on the usage of ML with traditional engineering approaches to use the right solution for the right problem
- Partner with Staff Engineers and Product partners, policy owners and operational teams, to identify and demonstrate ML opportunities. Encourage critical thinking within teams to develop this understanding themselves
- 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
Who You Are
- 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 see this educational opportunity as a key part of your role and always seek to help others understand and learn
- 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
Where You'll Be
- We are a distributed workforce enabling our band members to find a work mode that is best for them! For the right candidate anywhere we support in EMEA is possible however we have a preference for a candidate in Stockholm, London or Dublin where many of CoCaM members are located
- Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work Work From Anywhere options here
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
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Last updated on Oct 2, 2024