Senior ML Operations (MLOps) Engineer

About the role

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.

Responsibilities

• 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
• Collaboration: Partner with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales to Pods everywhere
• Optimization: Drive cost-effective, scalable, and high-performance ML systems by optimizing compute, storage, and deployment resources across training and inference
• Tooling Development: Develop tooling, micro services, and frameworks to streamline data processing, experimentation, and deployment

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

Qualifications

• 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

Skills

• Software Engineering
• Machine Learning Infrastructure
• Distributed Systems
• Data Processing
• Python (e.g., PyTorch, TensorFlow)
• Cloud Native Technologies (AWS, Lambda, ECS, DynamoDB, CloudWatch)
• Real-Time ML Workflows and Streaming Systems (Kinesis, Kafka, Flink)
• CI/CD Pipelines for Model Deployment
• Secure ML Operations, Privacy Practices, and Compliance Considerations
• Health, Wellness, or IoT Domains (Wearables, Medical-Grade Devices)

Benefits

• Immediate Responsibility and Accelerated Career Growth
• Periodic Equity Refreshments Based on Performance
• Full Access to Health, Vision, and Dental Insurance for You and Your Dependents
• Supplemental Life Insurance
• Flexible PTO
• Commuter Benefits to Ease Your Daily Commute
• Paid Parental Leave

Pay

Competitive compensation package with equity participation.

Schedule

Remote work environment with flexible hours.