Machine Learning Engineer, Infrastructure

About Rad AI

We have raised $80+ million to date from venture funds and just closed on our series B financing with investors Khosla Ventures, Gradient (Google’s AI fund) and ARTIS. We’ve also formed a partnership with Google to collaborate on the future of generative AI to redefine healthcare. Currently, more than 1/3 of radiology groups and healthcare systems, including Kaiser Permanente, HCA Healthcare, and Geisinger, now leverage the latest Gen AI advancements from Rad AI. We're recognized as one of the most promising healthcare AI companies by both CB Insights and AuntMinnie. Come join us in transforming healthcare with AI!

Founded by the youngest US radiologist in history, Rad AI empowers physicians with Al to save time, reduce burnout, and improve the quality of patient care. By combining our deep expertise in healthcare and AI and using one of the largest proprietary radiology report datasets in the world, our AI has uncovered hundreds of new cancer diagnoses for patients and reduced the error rate in tens of millions of radiology reports by nearly 50%. 

Why Join Us: 

Given our large client growth and projected movement in the year ahead, we are seeking an experienced Machine Learning Engineer to join our team. The candidate that we are looking for will have expertise in maturing, scaling and optimizing the infrastructure of a quickly growing product, and a passion for building, teaching, learning, and collaborating in a high-performing cross functional team working to make a difference in millions of patients and physicians lives.

What You’ll Be Doing:

• Design, implement, and maintain the infrastructure that supports our machine learning applications, services, and workflows 

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

• Leverage low-level programming languages, cloud native services, and serverless architectures to build scalable and resilient systems

• Plan, design and develop components in the data pipeline to enable various machine learning models in production

• Lead the design and implementation of infrastructure projects, including the development of technical designs, plans, and specifications, along with their evolutions and updates

• Design, deploy, and maintain the full ML platform stack including capabilities such as monitoring and data observability, the full model lifecycle, etc.

• Investigate the existing pipeline, identify bottlenecks and optimize the throughput and latency of ML components

• Balance metrics and alerting with cost efficiency and detail

• Develop and implement automation tools for model …