Design and operate scalable ML infrastructure, including data pipelines, model training, and production inference systems. Collaborate with research and engineering teams to ensure reliable, cost-effective, and secure deployment of machine learning models.
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
SumerSports is committed to fair and equitable compensation practices.
Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.
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