Staff MLOps Platform Engineer at WHOOP

About the Role

We are seeking a Staff MLOps Platform Engineer to join the Data Platforms & MLOps Team. This role focuses on building durable machine learning platforms that enable teams across WHOOP to develop, deploy, and operate models safely and at scale. You will design systems that prioritize reliability, observability, and developer velocity, serving as the backbone for WHOOP's machine learning ecosystem.

Key Responsibilities

• →Architect, build, own, and operate scalable ML infrastructure in cloud environments (e.g., AWS), optimizing for speed, observability, cost, and reproducibility.
• →Create, support, and maintain core MLOps infrastructure (e.g., MLflow, feature store, experiment tracking, model registry), ensuring reliability, scalability, and long-term sustainability.
• →Develop, evolve, and operate MLOps platforms and frameworks that standardize model deployment, versioning, drift detection, and lifecycle management at scale.
• →Implement and continuously maintain end-to-end CI/CD pipelines for ML models using orchestration tools (e.g., Prefect, Airflow, Argo Workflows), ensuring robust testing, reproducibility, and traceability.
• →Partner closely with Data Science, Sensor Intelligence, and Data Platform teams to operationalize and support model development, deployment, and monitoring workflows.
• →Build, manage, and maintain both real-time and batch inference infrastructure, supporting diverse use cases from physiological analytics to personalized feedback loops for WHOOP members.
• →Design, implement, and own automated observability tooling (e.g., for model latency, data drift, accuracy degradation), integrating metrics, logging, and alerting with existing platforms.
• →Leverage AI-powered tools and automation to reduce operational overhead, enhance developer productivity, and accelerate model release cycles.
• →Contribute to and maintain internal platform documentation, SDKs, and training materials, enabling self-service capabilities for model deployment and experimentation.
• →Continuously evaluate and integrate emerging technologies and deployment strategies, influencing WHOOP's roadmap for AI-driven platform efficiency, reliability, and scale.