Senior ML Operations (MLOps) Engineer

Join our team as a Sr MLOps Engineer to help us bring current and next generations of Pod ML models to life. You'll be a part of a small team designing and implementing solutions with high levels of autonomy to bring our members better sleep. Your work will go directly to our fleet of existing Pods with low friction and direct impact to the business. We are a fast moving and fast growing company, and we embrace individuals with a growth mindset and strong desire to help us achieve our mission: Improving people's lives through optimal slee

How you’ll contribute

• 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.

What you need to succeed

• 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.

What sets you apart:

• 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.