Data Engineer, Machine Learning

Berkeley, CA /
Engineering /
Full-time
Rad AI applies deep learning to radiology in order to save lives and reduce the cost of healthcare. We believe that strong teams working closely together create audacious companies that transform our world for the better.

Our world-class team of engineers is building and deploying products that will make a difference in millions of people’s lives. You’ll be an early team member, helping us shape the long-term vision for Rad AI’s future. Our benefits include health, dental and vision coverage, unlimited paid vacation, catered lunches, and unlimited snacks.

Here at Rad AI, we’re focused on transparency, inclusion, close collaboration, and building an incredible team. Come and help us make a difference!


This is what you'll do:

    • Maintain and expand data cleaning and manipulation codebase
    • Align with the machine learning team to ensure data integrity and cleanliness within machine learning models
    • Evaluate and propose the best tooling and processes for data access and analysis
    • Provide design and review support to internal teams working on data processing

This is what you need:

    • B.S. in computer science or equivalent experience
    • 1+ years professional software engineering experience
    • Extensive knowledge of Python and the tooling around it
    • Team oriented individuals that value transparency and working in a data driven environment

This would be good to have:

    • Experience or interest in machine learning projects and healthcare
    • Experience writing data pipelines using technologies such as PySpark


Thank you for your interest. We look forward to hearing from you.

At Rad AI, we value diversity and provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.