MLOps, ML Platform Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and manage ML infrastructure for data processing, training, serving, and inference systems.

• Implement reproducible training and evaluation pipelines that include versioning, scheduling, and artifact tracking.

• Optimize GPU and CPU workloads, oversee clusters, and enhance compute costs through rightsizing, spot scheduling, and caching strategies.

• Operate production APIs facilitating low-latency inference with features like autoscaling, blue-green or canary rollouts, and rollback safety.

• Define and take ownership of Service Level Objectives (SLOs); instrument pipelines and services to monitor latency, costs, drift, and data quality.

• Manage Identity and Access Management (IAM), secrets, and container security.

• Automate deployment pipelines utilizing CI/CD and infrastructure as code methodologies.

• Collaborate with research scientists and AI engineers to transition models from experimentation to production.

• Create templates, runbooks, and internal tools to ensure ML workflows are repeatable, secure, and efficient.

⛳️ Requirements

• Minimum of 4 years of experience in ML platform, DevOps, or infrastructure engineering.

• In-depth knowledge of Kubernetes, CI/CD, containerization, and cloud infrastructure (AWS, GCP, or Azure).

• Practical experience in managing GPU clusters and training/inference pipelines.

• Familiarity with data orchestration and storage formats such as Delta, Parquet, Polars, and Spark.

• Demonstrated ability to deploy and maintain production ML systems adhering to SLOs.

• Strong Python programming skills and proficiency in infrastructure as code and automation practices.

• Experience with observability and cost optimization at scale.

• Background in real-time or low-latency model serving (REST, gRPC).

• Exposure to model registry and promotion workflows.

• Understanding of data quality, lineage, and curation pipelines.

• Experience in sports analytics or similar high-volume data fields.

• Familiarity with 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 the Monday after the Super Bowl.

• Annual performance bonus.

• Benefits and/or other applicable incentive compensation plans.