Machine Learning Engineering Team Lead, Prenuvo

• If you're passionate about the intersection of cutting-edge ML engineering and impactful healthcare outcomes, join us on our mission to change lives through early diagnosis
• 5+ years of experience in software engineering or ML infrastructure, including 2+ years in a technical leadership or team lead role
• Strong proficiency in Python, and deep experience with its ecosystem for packaging, testing, and deploying production-quality ML code (e.g., Docker, Pytest, Poetry, etc.)
• Expertise in designing and operating systems for ML inference, including experience with scheduling tools (e.g., Airflow, custom job queues, etc.)
• Hands-on experience with container orchestration and cloud-native infrastructure
• Experience with ML workflow platforms such as SageMaker and emerging distributed computing frameworks like Ray or AnyScale, or equivalent. Understanding of how to scale ML inference across compute clusters efficiently
• Experience building APIs or services to expose ML predictions to other teams or applications
• Strong cross-functional communication skills and a product mindset—you think in terms of outcomes and system impact, not just deployments
• Comfort working in fast-moving environments with evolving requirements, and a high level of ownership

Desirable

• As we scale our technology and data capabilities, machine learning is central to delivering intelligent health insights at population scale
• We are looking for a Team Lead, Machine Learning Engineering as we're investing in a world-class MLOps function to build robust infrastructure that supports secure, reproducible, and scalable ML systems
• Help reshape the world through proactive healthcare while working with cutting-edge technology and high performing teams with deep expertise - join us to make a difference in people’s lives!
• Inference Infrastructure: Design and maintain secure, scalable systems for running ML inference across production environments
• Job Scheduling: Build and operate job scheduling infrastructure to manage inference workloads at scale, including a migration from AWS SageMaker to AnyScale/Ray to support distributed execution, greater flexibility, and internal observability
• Prediction Storage & Access: Develop and maintain services to store, version, and serve predictions for internal analytics and downstream systems
• Systems Integration: Work closely with Software Engineering teams to expose predictions via secure APIs and support reporting, visualization, and clinical integration
• Collaboration with AI Research: Partner with ML researchers to productionize trained models, support model packaging, and ensure compatibility with runtime systems
• Observability & Monitoring: Own observability for all deployed ML systems—logging, metrics, alerting, and drift monitoring
• Engineering Leadership: Mentor and grow a team of engineers focused on building and supporting ML-powered services and infrastructure
• Compliance & Documentation: Ensure infrastructure is auditable and compliant with healthcare standards (e.g., HIPAA, FDA) and support documentation for regulatory submissions