Senior MLOps Engineer - Remote Position at Eight Sleep

About the Role

Join us as a Senior MLOps Engineer to help us bring current and next generations of Pod ML models to life. This Senior MLOps Engineer role offers the opportunity to work in a fast-paced environment where you will contribute to improving people's lives through optimal sleep. You will be part of a small team designing and implementing solutions with high levels of autonomy to bring our members better sleep.

What You'll Do

• Pioneer Cutting-Edge Technology: Introduce and implement cutting-edge ML technologies, integrating them into our products and processes to enable the future of health monitoring.
• End-to-End Ownership: Own design and operation of robust ML infrastructure – building scalable data, model, and deployment pipelines that ensure reliable delivery of models to production.
• Cross-functional Collaboration: Partner with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales to Pods everywhere.
• Optimize for Performance: Drive cost-effective, scalable, and high-performance ML systems by optimizing compute, storage, and deployment resources across training and inference.
• Enhance Tooling and Platforms: Develop tooling, micro services, and frameworks to streamline data processing, experimentation, and deployment.
• Effective Remote Communication: Thrive in a remote work environment, ensuring clear and direct communication.

Requirements

• Proven Expertise: 5+ years of software engineering experience with a focus on ML infrastructure, distributed systems, or large-scale data processing in Python (e.g., PyTorch, TensorFlow, or similar).
• ML Operations Mastery: Hands-on experience with ML workflow orchestration and CI/CD pipelines for model deployment.
• Scalable Deployment Experience: Demonstrated success shipping ML models to production at scale, handling telemetry, monitoring, and feedback loops across large device fleets or user populations.
• Cloud-Native Expertise: Strong experience with AWS (Lambda, ECS, DynamoDB, CloudWatch) or equivalent cloud platforms for serving and monitoring ML systems.
• Adaptive Problem Solver: A fast-paced, collaborative, and iterative approach to tackling complex problems.

Nice to Have

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

What We Offer

• Equitable compensation and continuous equity investment.
• Immediate responsibility and accelerated career growth.
• Your own Pod – and other great benefits including health, vision, and dental insurance, flexible PTO, and paid parental leave.
• Join a culture of innovation where excellence is the standard.
• Work alongside exceptional talent in a dynamic environment.