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Senior ML Platform Engineer

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Whoop

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Location:
United States, Boston

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

150000.00 - 210000.00 USD / Year
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Job Description:

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 highly skilled Senior ML Platform Engineer to join our ML Platform team. This role is pivotal in scaling our ML infrastructure and enabling the efficient deployment and monitoring of machine learning models across cloud environments. As a senior contributor, you will act as a force multiplier—building robust abstractions, platforms, and tooling to supercharge our Data Science and AI teams. You will design and implement scalable systems that operationalize machine learning at WHOOP with speed, reliability, and precision. Your work will directly support our use of AI to enhance team efficiency, automate decision-making, and personalize member experiences.

Job Responsibility:

  • 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

Requirements:

  • Bachelor’s or Master’s 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
What we offer:
  • equity
  • benefits

Additional Information:

Job Posted:
December 13, 2025

Employment Type:
Fulltime
Work Type:
On-site work
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