Staff Machine Learning Engineer - Infrastructure

About Rad AI

At Rad AI, we’re on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI-driven solutions are revolutionizing radiology—saving time, reducing burnout, and improving patient care. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%.

Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others—all backing our mission to empower physicians with cutting-edge AI.

Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one-third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health.

Recognized as one of the most promising healthcare AI companies by CB Insights and AuntMinnie, and ranked by Deloitte as the 19th fastest-growing company in North America, we are building AI-powered solutions that make a real impact. Most recently, Rad AI was named to CNBC’s Disruptor 50 list, highlighting the innovation and momentum behind our mission.

If you’re ready to shape the future of healthcare, we’d love to have you on our team!

Why Join us?

We’re looking for a Staff Machine Learning Engineer to join our MLOps team and help build and maintain the infrastructure that supports our cutting-edge AI research and products. In this role, you’ll develop tools and systems that accelerate language model R&D and serve those models to radiologists, ultimately improving clinical outcomes for patients.

You’ll play a key role in designing and implementing the infrastructure that connects our models to our customer-facing products. This role is backend-focused and will primarily include development in Python.

This is a unique opportunity to work at the intersection of AI and healthcare, shaping the future of how radiologists care for patients.

What You’ll Be Doing:

• Architect the infrastructure that supports our machine learning applications, services, and workflows

• Architect and maintain our ML platform that supports continuous integration, continuous delivery, and continuous training for our machine learning models

• Develop cloud-native services and serverless architectures to build scalable and resilient systems

• Partner with data scientists to design the data pipeline that enable various machine learning models in production

• Write code that meets our internal standards for security, style, maintainability, and best practices for a high-scale HIPAA web environment

• Design, deploy, and maintain the full ML platform stack including monitoring and observability, data analytics, backend integration with customer-facing products, and the full model R&D lifecycle

• Work with Product Management, Research, and Engineering to iterate on new features and address inefficiencies across our AI/ML infrastructure

Who We’re Looking For:

• 8+ years of industry experience in ML Engineering in cloud-native environments

• In-depth knowledge of Python (required), Javascript/Typescript (nice to have), or other modern languages in the ML domain

• Strong experience with infrastructure and DevOps tools such as Kubernetes, Docker, and Ansible

• Strong knowledge of cloud computing platforms such as AWS (preferable), GCP, and Azure

• Experience architecting distributed systems, storage systems, and databases

• Experience working with machine learning frameworks such as PyTorch and LangGraph

• Experience with Airflow (preferable) or other orchestration tools

• Experience with infrastructure-as-code tools such as Terraform (preferable), Pulumi, Cloud Formation, etc.

• Experience with monitoring, tracing, and logging tools such Cloudwatch, NewRelic, G