Head of Quality Engineering

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!

Radiologists use our products every day to produce the reports that decide what care a patient gets next. A defect here isn't an inconvenience; it's a blank report after signing, a missing accession, or a formatting error in a clinical impression. That's not a context where "good enough" ships.

We already have a strong, embedded quality team across Reporting, Impressions, Continuity. and it's stronger than our current quality outcomes show. Some areas already run PR-level checks, developer-written end-to-end tests, AI-assisted test creation, and automated regression triage. The gap is consistency: practices and maturity vary by product area, and the quality customers experience doesn't yet match the work happening inside.

Our principle is simple: Engineering owns quality; Quality owns the quality system. You'll lead that system, spreading what already works rather than rebuilding it. You'll make release risk explicit, bring real radiologist workflows and customer environments into how we test, and make quality visible from the team level up to the executive team. The team you build will define how clinicians trust AI in their daily work.

You won't start from zero. You'll inherit a three-phase quality roadmap already underway, an initial quality baseline, and a revamped weekly incident review, and you'll own where they go next.

You'll lead Rad AI's Quality organization, reporting to the CPO, where you'll:

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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.