Machine Learning Platform Engineer
$155,000–$185,000 year
RemoteUnited States
Job Summary
Design and build end-to-end machine learning infrastructure to transition experimental models into high-availability production services. Deploy low-latency inference services in milliseconds to power real-time decisions across dynamic oddsmaking, risk analysis, and smart deposit defaults. Lead the creation of a centralized feature store bridging batch historical data with real-time event streams. Operate core ML platform components for training and experimentation, implementing automated retraining pipelines and observability to catch data drift instantly. Champion best practices for model deployment, monitoring, and CI/CD while enabling self-service for data science teams.
Required Qualifications
- 3+ years of experience in Platform Engineering
- Proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments
- 1+ years of experience owning ML systems end-to-end in production, including on-call and incident response
- Experience with Real-Time Data
- Proficiency in streaming architectures (Kafka/Flink/PubSub)
- Experience building low-latency services to serve model inference in <100ms
- MLOps Expertise
- Deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring)
- Experience using tools like SageMaker, VertexAI, Vector DBs, Graph Databases
- Experience managing and scaling caches like Redis or Elasticsearch
- Proficiency with Containerization
- Experience with Docker
- Experience with Kubernetes
- Experience with cluster-level management
- Expert in Python
- Proficiency in Go, C++, or Rust
Desired Qualifications
- Experience implementing infrastructure while enforcing best practices for the deployment of ML Platform
- Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading
- Experience building and scaling 'Feature Stores' that successfully bridge batch historical data with real-time event streams
- Experience enabling self-service for ML and Data Science teams for model development and deployment
- Experience enabling AI agents and AI coding for faster and iterative software development
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