Job Description
📋 Description Build scalable ML systems and end-to-end infrastructure for production-ready models. Enable real-time inference with low latency across DFS/oddsmaking platforms. Lead feature store initiatives bridging batch data with real-time streams. Collaborate with Infrastructure to deploy ML platform components and CI/CD for ML. 🎯 Requirements 3+ years in Platform Engineering with scalable ML platforms in production. Experience owning ML systems end-to-end (on-call, incident response). Real-time data experience with streaming architectures (Kafka/Flink/PubSub). ML lifecycle tooling: SageMaker, VertexAI; caching (Redis/Elasticsearch). Proficient in Python; experience with Go; containerization (Docker, Kubernetes). 🎁 Benefits Medical, dental, vision plans; company-subsidized coverage. 401(k) with company match; annual bonus. Flexible PTO; parental leave; disability benefits. Remote-friendly with in-person team events; equipment provided.
