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.

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