MLOps / ML Platform Engineer

As an MLOps/ML Platform Engineer, you’ll build and operate the core systems that power our machine learning and AI workloads across sports domains. You’ll own the infrastructure that keeps our models fast, reliable, and cost-efficient — from data ingestion and training to model serving, deployment, and observability. This is a hands‑on engineering role that blends software infrastructure, distributed systems, and machine learning productionization. You’ll work closely with our Deep Learning Research, LLMOps, and Product Engineering teams to ensure that every model we build can be trained, deployed, and monitored at scale. Responsibilities Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high‑throughput model workflows. Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking. Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching. Serve models in production: Operate APIs for low‑latency inference with autoscaling, blue‑green or canary rollouts, and rollback safety. Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality. Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code. Collaborate cross‑functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction. Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast. Qualifications 4+ years of experience in ML platform, DevOps, or infrastructure engineering. Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure). Hands‑on experience managing GPU clusters and training/inference pipelines. Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark). Proven ability to ship and operate production ML systems with SLOs. Strong Python skills and comfort with infrastructure as code and automation. Experience with observability and cost optimization at scale. Nice to Have Experience with real‑time or low‑latency model serving (REST, gRPC). Exposure to model registry and promotion workflows. Familiarity with data quality, lineage, and curation pipelines. Background in sports analytics or other high‑volume data domains. Experience integrating LLM workflows or evaluation pipelines. Benefits Competitive Salary and Bonus Plan Comprehensive health insurance plan Retirement savings plan (401k) with company match Remote working environment A flexible, unlimited time off policy Generous paid holiday schedule – 13 in total including Monday after the Super Bowl #J-18808-Ljbffr sumersports