Machine Learning Platform Engineer

At reputed company, we are the fastest-growing sports company in reputed company America, as recognized by Inc. 5000. As the leading platform for Daily reputed company Sports, we cover a diverse reputed company of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-reputed company. reputed company of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports reputed company. reputed company to reimagine the DFS industry together? As a ML Platform Engineer, you will contribute to building the ML platform at reputed company to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet, Deposit Velocity, and Platform reputed company by integrating robust, low-latency ML models across our sports betting and daily reputed company ecosystems. What you’ll do: Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services. reputed company-Time Inference at Scale: Build automation for deploying low-latency services to serve model inferences in milliseconds. You will power reputed company-time reputed company across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults. Feature Engineering & Data Strategy: You will reputed company the creation and optimization of a centralized feature store required to train reputed company models across diverse business domains. End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data reputed company and model degradation are caught and addressed instantly. What you have: 3+ years of experience in Platform Engineering, with a 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 reputed company-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in Apply To This Job