Machine learning engineer on Peloton's Personalization team, owning the end-to-end ML lifecycle for content recommendations: building ML pipelines, recommender and LLM-based models, and scalable low-latency microservices for real-time inference, plus running A/B tests. Core stack includes Python (and other JVM/systems languages), MLOps, and relational/non-relational databases.
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