Staff MLOps Platform Engineer

Whoop•Boston, MA

2d•$170,000 - $230,000•Onsite

About The Position

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. 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 WHOOPâs machine learning ecosystem. In this role, you will operate as a senior individual contributor with broad platform ownership. Your work will multiply the effectiveness of data scientists and ML engineers by abstracting complexity, establishing standards, and creating self service capabilities that support experimentation and production workloads across the company.

Requirements

Bachelorâs or Masterâs Degree in Computer Science, Engineering, or a related field; or equivalent practical experience.
5+ years of experience in software engineering with a focus on ML infrastructure, cloud platforms, or MLOps.
Strong programming skills in Python, with experience in building distributed systems and REST/gRPC APIs.
Deep knowledge of cloud-native services and infrastructure-as-code (e.g., AWS CDK, Terraform, CloudFormation).
Hands-on experience with model deployment platforms such as AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks.
Proficiency in ML lifecycle tools (MLflow, Weights & Biases, BentoML) and containerization strategies (Docker, Kubernetes).
Understanding of data engineering and ingestion pipelines, with ability to interface with data lakes, feature stores, and streaming systems.
Proven ability to work cross-functionally with Data Science, Data Platform, and Software Engineering teams, influencing decisions and driving alignment.
Passion for AI and automation to solve real-world problems and improve operational workflows.

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 WHOOPâs roadmap for AI-driven platform efficiency, reliability, and scale.