MLOps – ML Platform Engineer

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

📋 Description

• Design and manage machine learning infrastructure for data handling, training, serving, and inference systems.

• Develop scalable and reproducible training and evaluation pipelines featuring version control, scheduling, and artifact tracking.

• Optimize GPU and CPU workloads, oversee cluster management, and enhance efficiency through rightsizing, spot scheduling, and caching.

• Administer production model APIs for low-latency inference, incorporating autoscaling, blue-green or canary rollouts, and rollback safety.

• Establish and take ownership of service level objectives (SLOs); instrument pipelines and services to monitor latency, costs, drift, and data quality.

• Oversee identity and access management (IAM), secrets, and container security.

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

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

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

⛳️ Requirements

• A minimum of 4 years of experience in machine learning platforms, DevOps, or infrastructure engineering.

• Extensive knowledge of Kubernetes, CI/CD, containers, 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 ship and operate production ML systems with defined SLOs.

• Proficient in Python and comfortable with infrastructure as code and automation practices.

• Experience with observability and cost optimization on a large scale.

• Familiarity with real-time or low-latency model serving (REST, gRPC) is considered a plus.

• Exposure to model registry and promotion workflows is advantageous.

• Knowledge of data quality, lineage, and curation pipelines is a plus.

• Background in sports analytics or other high-volume data sectors is a plus.

• Experience in integrating LLM workflows or evaluation pipelines is beneficial.

🏝️ 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