Viz.ai builds clinical AI that detects disease early and routes patients to care teams faster. Its stack pairs classic deep learning (PyTorch, TensorFlow) with an LLM layer (Amazon Bedrock, Hugging Face Transformers, spaCy), and job posts point to LLM-driven medical text insights and agentic skills. Accelerating, senior-weighted hiring across engineering, sales and product, plus a Product Director search, signals a push into oncology.
Notable leadership hires: Product Director, Business Development Director
Viz.ai, founded in 2016 and headquartered in San Francisco, develops an AI platform for disease detection and clinical care coordination. It sells to hospitals and health systems across the United States and also works with life sciences companies. The platform focuses on care pathways in areas such as stroke, aneurysm and radiology, with oncology an active area of development. The privately held company has 201–500 employees and hires in the United States and Israel. Its infrastructure runs on AWS, using EKS, ECS and RDS, with Kubernetes, Terraform and Helm.
Viz.ai runs on AWS (EKS, ECS, RDS, Bedrock) with Python, PyTorch, TensorFlow, Hugging Face Transformers, Kubernetes, Terraform, Temporal, RabbitMQ, Redis and OpenSearch, plus Salesforce and Pardot for go-to-market.
Job posts point to clinical decision support AI algorithms, EHR integrations, AI-based medical imaging annotation, LLM-driven insights from medical text, and oncology product development and care pathways.
Viz.ai hires in the United States and Israel. Its hiring is accelerating, with the focus on senior engineering, sales and product roles, including Product Director and Business Development Director positions.
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Viz.ai's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
