We’re part of Spotify’s Podcast Mission, focused on giving podcast creators tools to grow their audiences and connect more deeply with listeners. The team is building the systems that power interactive podcast experiences, enabling everything from polls, comments, and Q&A to emerging agent-driven features.
As a Senior Machine Learning Engineer, from integrating foundational models to crafting feedback loops for real-time interactivity, you'll shape how creators and consumers connect in more dynamic and intelligent ways. This is a product-oriented role where you’ll spend time understanding roadmaps and advising leadership on the trade-offs of implementing ML solutions. You’ll also play a key role in build-vs-buy decisions, assessing the engineering effort required, and connecting with ML teams across Spotify to find opportunities for collaboration with the Podcast mission.
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What You'll Do- Lead the ML strategy for a 200+ person organization by defining and maintaining a clear roadmap for the Spotify for Creators and Megaphone apps, collaborating closely with engineers and PMs to align on product requirements and enhance impact.
- Advise leadership on ML initiatives, guiding prioritization and shaping decisions on the development and rollout of ML features.
- Serve as the liaison ML efforts within the Podcast mission and ML engineers/PMs across Spotify, ensuring deep understanding of existing systems and facilitating their seamless integration into the Spotify for Creators product.
- Mentor engineers in ML practices to level up Podcast Mission’s capabilities in the space.
- Work closely with backend, data, and client engineers as well as PMs and designers to ship features that directly impact podcast engagement metrics.
- Prototype novel agentic workflows (e.g., multi-component pipelines or tool-using agents) and contribute to experimentation around Creator-Consumer interactivity.
- Own and evolve ML model lifecycle: data annotation, data pipeline construction, feature engineering, model training, deployment, and monitoring.
- Design, develop, fine-tune, and deploy machine learning systems that power podcast growth.
- Optimize models for scale and reliability using modern ML infra tools like Ray, Apache Beam, and Google Cloud Platform (GCP).
- Lead with an experimentation attitude. Implement A/B tests and contribute to continuous model evaluation and improvement loops to productionize solutions at scale for our millions of active podcast users.
- Participate in Spotify’s ML community: share findings, explore new tools and paradigms, and contribute to scaling ML standards across the company.
Who You Are- You have 5+ years of professional experience in machine learning, with expertise in building and productionize ML systems at scale.
- You’re fluent in Python; experience with PyTorch or TensorFlow is a strong plus.
- You’ve worked with large-scale data systems and owning end-to-end ML workflows (data ingestion to serving).
- You have hands-on experience with cloud platforms like GCP or AWS, and with ML infra tools such as Ray, Apache Beam, or Airflow.
- You have experience, or strong interest, in agent-based systems and LLM integrations.
- You thrive in agile environments, care deeply about product impact, and bring a user-centered outlook to ML development.
- Bonus: Experience with content recommendation, interactive media formats, or real-time systems.
- Bonus: You’ve led initiatives within an organization, expertly balancing trade-offs in large-scale systems, engaging with collaborators, and crafting clear, actionable roadmaps.
Where You'll Be- We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
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The United States base range for this position is $ 176,166 - $ 251,666, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, a monthly meal allowance, 23 paid days off, 13 paid flexible holidays. These ranges may be modified in the future.
