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

The Verily precision health platform is the comprehensive and patient-centered engine that accelerates the evidence generation needed for safer, more effective treatments and care decisions, helping people live longer, healthier lives. Our cloud-first approach, advanced analytics, and AI expertise enable us to collect, organize, enrich, and activate petabytes of data aligned with our customers' unique needs and delivered at just the right time. As a member of the Precision Health Platform engineering organization, you will build modular, composable, and interoperable AI platform components, including the development and maintenance of software for ML applications (data science, computer vision, and LLMs).

Our team is responsible for integrating data from a variety of sources (e.g., EHR, ePROs, digital biomarkers, devices) and building the AI/ML infrastructure necessary to provide curated datasets and insights that contribute to clinical and research goals. In addition to research, we are building AI tools for our care products to improve the patient experience, with a focus on agent infrastructure, RAG (Retrieval-Augmented Generation) systems, and specialized components like embedding stores and LLM orchestration tools such as LangGraph. Be at the forefront of innovation and tackle exciting challenges within a collaborative and dynamic work environment.

Responsibilities

• Design and develop highly scalable AI/ML platform components, taking ownership of our model serving infra, agentic infrastructure and context-grounded generation (RAG) pipelines

• Orchestrate LLM workflows by integrating specialized AI components, such as vector and embedding stores and orchestration frameworks like LangGraph, into production-grade care products.

• Architect secure and robust data integration pipelines capable of organizing and activating diverse healthcare data (EHRs, ePROs, digital biomarkers) into curated datasets for AI/ML models.

• Collaborate with cross-functional teams to translate complex clinical and research goals into technical specifications, taking ownership of design oversight, scoping, and project deliverables.

• Drive engineering best practices through rigorous code reviews, automated testing, and comprehensive troubleshooting to ensure the reliability of cloud-first AI applications.

Qualifications

Minimum Qualifications

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

• 7+ years of professional experience in software engineering, cloud engineering, or ML engineering (or 4+ years of experience with an advanced degree).

• Proficiency in at least one major programming language used in AI and cloud infrastructure (e.g., Python, Go, or Java).

• Proven track record of designing, building, and delivering high-impact, production-quality software in a cloud environment.

• Hands-on experience with ML platforms, pipelines, and tooling (e.g., Vertex AI, Kubernetes/GKE, Kubeflow, or equivalent).

Preferred Qualifications

• Experience configuring CI/CD pipelines and infrastructure-as-code tools like Terraform and GitHub.

• Demonstrated experience in building robust model training, evaluation, and deployment environments in the cloud.

• Hands-on experience building and deploying generative AI applications, specifically utilizing RAG architectures and vector/embedding stores.

• Familiarity with LLM orchestration and agent frameworks, such as LangGraph, LangChain, or LlamaIndex, in a production environment.

• Familiarity with healthcare data standards or experience working in highly regulated, compliance-driven environments.

Qualified applicants must not require employer sponsore