Note: The job is a remote job and is open to candidates in USA. SumerSports is a football intelligence technology company that provides data-driven products and insights to football fans, NFL clubs, and the NCAA. The MLOps / ML Platform Engineer will design and operate machine learning infrastructure, build scalable training and evaluation pipelines, serve models in production, optimize compute and costs, and ensure system reliability, security, and observability while collaborating with research scientists and AI engineers.
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
Skills
- 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
- 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
Company Overview