Senior Software Engineer, ML Infra

Verily•Mountain View, CA

About The Position

Verily, an Alphabet subsidiary, is dedicated to transforming health management and healthcare delivery through a data-driven approach, aiming to bring precision health to everyone. Launched from Google X in 2015, Verily focuses on generating and activating data from diverse sources—clinical, social, behavioral, and real-world—to provide optimal health solutions based on comprehensive evidence. Leveraging expertise in technology, data science, and healthcare, Verily empowers the healthcare ecosystem to achieve superior health outcomes. The Verily precision health platform is a comprehensive, patient-centered engine designed to accelerate evidence generation for safer, more effective treatments and care decisions, ultimately fostering longer, healthier lives. This platform utilizes a cloud-first approach, advanced analytics, and AI expertise to collect, organize, enrich, and activate petabytes of data tailored to customer needs and delivered precisely when required. As a member of the Precision Health Platform engineering organization, you will be responsible for building modular, composable, and interoperable AI platform components, including the development and maintenance of software for ML applications spanning data science, computer vision, and LLMs. The team integrates data from various sources such as EHRs, ePROs, digital biomarkers, and devices, and constructs the necessary AI/ML infrastructure to deliver curated datasets and insights for clinical and research objectives. Additionally, the team develops AI tools for care products to enhance the patient experience, with a particular focus on agent infrastructure, RAG (Retrieval-Augmented Generation) systems, and specialized components like embedding stores and LLM orchestration tools such as LangGraph. This role offers an opportunity to be at the forefront of innovation and tackle exciting challenges within a collaborative and dynamic work environment.

Requirements

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).
Qualified applicants must not require employer sponsored work authorization now or in the future for employment in the United States.

Nice To Haves

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.

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.