MLOps – ML Platform Engineer

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SumerSports

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

⚽ Sports

🤖 Artificial Intelligence

☁️ SaaS

Sports • Artificial Intelligence • SaaS

SumerSports is an AI-powered sports analytics and technology company focused on football (NFL and NCAA). Combining over 500 years of NFL experience with machine learning, SumerSports offers products such as SūmerBrain for film retrieval and multi-layered data, SūmerLive for game tracking, SūmerNFL and SūmerNCAA for roster building and team optimization, and a player-verified metrics and talent exposure platform. The company produces draft guides, analytics-driven content with former scouts and Hall of Famers, and tools that serve players, teams, and fans to improve scouting, roster decisions, and performance evaluation.

📋 Description

• Design and operate ML infrastructure for data, training, serving, and inference systems • Build scalable, reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking • Tune GPU and CPU workloads, manage clusters, and improve efficiency through rightsizing, spot scheduling, and caching • Operate production model APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety • Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality • Manage IAM, secrets, and container security • Automate deployment pipelines using CI/CD and infrastructure as code • Partner with research scientists and AI engineers to deliver models from experiment to production • Build templates, runbooks, and internal tooling to make ML workflows repeatable, safe, and fast

🎯 Requirements

• 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) is a nice-to-have • Exposure to model registry and promotion workflows is a nice-to-have • Familiarity with data quality, lineage, and curation pipelines is a nice-to-have • Background in sports analytics or other high-volume data domains is a nice-to-have • Experience integrating LLM workflows or evaluation pipelines is a nice-to-have

🏖️ 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 • Annual performance bonus • Benefits and/or other applicable incentive compensation plans