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EightsleepEightsleepUnited States

Senior ML Operations (MLOps) Engineer

Build and operate scalable ML infrastructure for deploying models to IoT sleep devices. Own end-to-end pipelines, optimize performance, and collaborate cross-functionally. Requires 5+ years in ML ops, Python, AWS, and production ML deployment.

Salary not listed
Remote5+ YOEML Engineering

About the role

Responsibilities

  • Pioneer cutting-edge ML technologies, integrating them into products and processes for health monitoring.
  • Own design and operation of robust ML infrastructure, building scalable data, model, and deployment pipelines.
  • Partner with R&D, firmware, data, and backend teams for reliable ML inference at scale.
  • Optimize compute, storage, and deployment resources for training and inference.
  • Develop tooling, microservices, and frameworks for data processing, experimentation, and deployment.

Requirements

  • 5+ years software engineering experience focused on ML infrastructure, distributed systems, or large-scale data processing in Python (e.g., PyTorch, TensorFlow).
  • Hands-on experience with ML workflow orchestration and CI/CD pipelines for model deployment.
  • Experience shipping ML models to production at scale, handling telemetry, monitoring, and feedback loops.
  • Strong experience with AWS (Lambda, ECS, DynamoDB, CloudWatch) or equivalent for serving and monitoring ML systems.
  • Adaptive problem-solving in fast-paced, collaborative environments.

Nice-to-Haves

  • Expertise in real-time ML workflows and streaming systems (e.g., Kinesis, Kafka, Flink).
  • Optimizing ML infrastructure for efficiency, latency, and cloud cost.
  • Understanding of secure ML operations, privacy, and compliance for health/IoT data.
  • Familiarity with health, wellness, or IoT domains, especially wearables or medical-grade devices.

Skills

PythonPyTorchTensorFlowAWSAWS LambdaECSDynamoDBCloudWatchCI/CDMl WorkflowsKubernetesKafkaKinesisFlink

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