Data Engineer

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!

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  Design and implement the data architecture, ensuring scalability, flexibility, and efficiency using pipeline authoring tools like Apache Airflow, AWS Glue and large-scale data processing technologies like Spark.

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  Define and extend our internal standards for style, maintenance, and best practices for a high-scale data platform.

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  Collaborate with researchers and other stakeholders to understand their data needs including model training and production monitoring systems and develop solutions that meet those requirements.

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  Take ownership of key data engineering projects and work independently to design, develop, and maintain high-quality data solutions.

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  Ensure data quality, integrity, and security by implementing robust data validation, monitoring, and access controls.

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  Evaluate and recommend data technologies and tools to improve the efficiency and effectiveness of the data engineering process.

• 5+ years relevant experience in data engineering.

• Expertise in designing and developing distributed data pipelines using big data technologies on large scale data sets.

• Deep and hands-on experience designing, planning, productionizing, maintaining and documenting reliable and scalable data infrastructure and data products in complex environments.

• Experience with various database technologies including SQL, NoSQL databases (e.g., AWS DynamoDB, ElasticSearch, Postgresql).

• Designed, built and maintained data infrastructure using IaC like Terraform.

• Prior Software Engineering experience is a plus.

• Experience working at an early stage startup.

• Experience in a HIPAA compliant environment.

• Experience working on machine learning or healthcare related projects.

Join our world-class team as we build and deploy AI solutions that empower physicians and transform patient care—making a meaningful impact on millions of lives. Driven by our mission,  we prioritize transparency, inclusion, and close collaboration, bringing together exceptional people to revolutionize healthcare. If you're passionate about driving innovation and delivering impactful healthcare solutions, we'd love to hear from you!

To learn more about what it's like to work at Rad AI, visit https://www.radai.com/life-at-rad-ai and be sure to follow us on LinkedIn to stay up to date!

For roles listed as San Francisco - Onsite:

• This role will be based in our San Francisco office and we expect employees to work onsite four days per week. The remaining time may be worked remotely or onsite, depending on team and business needs.

For roles listed as United States - Remote:

• This role is open to candidates located anywhere in the United States.

For roles listed as San Francisco - Onsite + United States - Remote:

• We will prioritize candidates who can work onsite four days per week in San Francisco, while also considering remote candidates located anywhere in the United States.