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.
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