Who We Are

Verily is a subsidiary of Alphabet that is using a data-driven approach to change the way people manage their health and the way healthcare is delivered. Launched from Google X in 2015, our purpose is to bring the promise of precision health to everyone, every day. We are focused on generating and activating data from a variety of sources, including clinical, social, behavioral and the real world, to arrive at the best solutions for a person based on a comprehensive view of the evidence. Our unique expertise and capabilities in technology, data science and healthcare enable the entire healthcare ecosystem to drive better health outcomes.

Description

Verily Pre is a comprehensive, patient-centered platform that accelerates evidence generation for safer, more effective treatments and care decisions. Our cloud-first architecture, advanced analytics, and AI capabilities enable the collection, enrichment, and activation of petabytes of data, tailored to meet the unique needs of our partners.

As Director of Software Engineering, you will lead a large organization responsible for the core infrastructure powering Verily’s AI capabilities and longitudinal health initiatives. You’ll oversee the Model Development Lifecycle, accelerate AI production through embedded engineering partnerships, and deliver critical platforms including Lifelong health studies and Customer Identity and Access Management (CIAM), while shaping technical strategy and fostering a collaborative, results-driven engineering culture.

Responsibilities

• Organizational Leadership: Build and lead a high-performing engineering organization of 40-50 engineers and managers. Define and drive the engineering culture, performance standards, and collaboration models across multiple cross functional teams.

• AI Infrastructure & Acceleration: Oversee the engineering strategy for the infrastructure at the core of Verily’s Model Development Lifecycle (MDLC), ensuring data scientists have the robust tooling required to prototype, validate, and deploy models efficiently.  You will champion the development of annotation and evaluation systems which enable rapid iteration and detect model hallucinations and bias.  Additionally you will drive the adoption of foundational model serving infrastructure that enables techniques such as RAG at scale.

• Cross-Functional Strategy: Partner deeply with Data Science, Clinical, and Product leadership to accelerate model development velocity and quality. Translate complex scientific requirements into scalable engineering roadmaps that support key evidence generation milestones.

• Longitudinal Solution Delivery: Direct the engineering of Verily’s Lifelong Health Study. Ensuring we seamlessly bridge research and care by capturing high-frequency sensor signals, supporting patient-centric applications like Verily Me, and integrating diverse real-world data streams.

• Identity & Security: Own the delivery and reliability of the platform’s Customer Identity and Access Management (CIAM) system. Ensure secure, compliant, and seamless access for patients, researchers, and health systems, maintaining our "comply-by-design" philosophy for privacy and data protection.

• Operational Excellence: Manage organizational resourcing, budgeting, and headcount planning to ensure teams are structured to dependably, sustainably, and quickly deliver on roadmaps. Lead improvements to cross-functional business processes and effectively manage tradeoffs between short-term execution and medium-term architectural investments.

Qualifications

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related technical field.

• 12+ years of experience in software, data, cloud, or ML engineering roles.

• 5+ years of experience managing engineering managers and leading technical strategy for multi-team organizations.

• Proven track record of building and leading teams responsible for large-scale data infrastructure, ML platforms, or regulated software products.

Preferred Qualifications

• Master’s degree or PhD in Computer Science or a related technical discipline.

• Deep understanding of "ML Ops" and responsible AI practices, including model governance, automated red-teaming, and evaluation frameworks..

• Experience designing and operating Identity and Access Management (IAM/CIAM) systems in a healthcare or highly regulated environment (e.g., HIPAA, GDPR).

• Familiarity with longitudinal health studies, sensor signal p