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Machine Learning Engineer for Content and Catalog
4 days ago
Delivering exceptional music experiences for millions of users worldwide requires a deep understanding of consumer expectations.
The ChallengeWe aim to enrich lives by bringing the music and audio people love to their devices, apps, and platforms every day.
The OpportunityAs a Staff Machine Learning Engineer on the Content and Catalog Management (CoCaM) team at TN Sweden, you will drive the definition, adoption, and expansion of ML usage within our content and catalogue management platform.
Key Responsibilities:- Develop and execute an ML strategy for content and catalog management across six squads, ensuring seamless and personalized experiences.
- Mentor and train 40+ engineers, engineering managers, and product managers in ML competence, promoting technical excellence.
- Partner with Staff Engineers, Product partners, policy owners, and operational teams to identify and demonstrate ML opportunities, fostering collaboration and openness.
- Cultivate strong relationships within the CoCaM team to promote collaboration, openness, and inclusion, driving high-quality and scalable solutions.
- Stay updated with the latest ML advancements, developing a learning environment to continuously improve the team's skills and knowledge.
- Proven track record of creating, promoting, and growing ML strategy for platforms, leveraging Java, Scala, Python, or similar technologies.
- Hands-on experience in implementing ML systems at scale, utilizing ML-specific libraries and frameworks like TensorFlow, PyTorch, or similar.
- In-depth knowledge of various ML algorithms, including supervised, unsupervised, and reinforcement learning, with experience communicating complex concepts to technical and non-technical audiences.
- Leadership experience in ML projects, mentoring more inexperienced engineers, and driving technical strategy and decision-making within teams.
- Experience with containerization and orchestration tools like Docker and Kubernetes, as well as cloud platforms such as GCP, AWS, or Microsoft Azure.
- Familiarity with model deployment techniques and serving frameworks like TensorFlow Serving, TorchServe, or custom APIs.
- Distributed workforce enabling work from anywhere, with options for working from home or office.
- Preference for candidates in Stockholm, London, or Dublin where many CoCaM members are located.
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