<aside> <img src="https://prod-files-secure.s3.us-west-2.amazonaws.com/bdd71ac3-f8a1-4acb-8bd0-16b9078d91b0/e1cfc3c7-312d-497b-9539-f95b67a85877/Screenshot_2024-09-07_at_2.04.50_PM.png" alt="https://prod-files-secure.s3.us-west-2.amazonaws.com/bdd71ac3-f8a1-4acb-8bd0-16b9078d91b0/e1cfc3c7-312d-497b-9539-f95b67a85877/Screenshot_2024-09-07_at_2.04.50_PM.png" width="40px" /> Quick facts about Radical 👋

Radical is building an AI cancer doctor that personalizes treatment for each individual.

• Sole access to the highest quality multi-modal hospital datasets in the U.S.
• This includes 6m patients, 142m clinical notes, linked radiology, pathology, and genetic data. It has never been shared before.
• Deployment lined up at the top cancer centre in the Bay Area.
• $4.5M pre-seed led by Tier 1 VCs in Silicon Valley. </aside>

What it’s like

Picture this: long hours, relentless problem-solving, and no guarantees. This isn’t your standard 9-5. We’re looking for people who want to build something generational. Go big or go home.

You could take a cushy job at a big company and make decent money. Here, every decision you make matters. Every line of code, every experiment, every late-night brainstorm could be the difference between life or death for a patient. If you’re the type who thrives on uncertainty, moves fast, ****loves a bit of delusion - you might just belong here.

If you’ve ever faced something terrifying and your first instinct was “LFG” - we should talk. At Radical, we’re reimagining the healthcare experience. It’s ambitious, exciting, and yeah, a lil crazy - but that’s how real change happens.

P.S. We skydive together and other (company expensed) things that would probably make HR nervous!

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Open roles

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All roles are on-site in San Francisco 🌉

Founding AI Engineer @ Radical

Founding Data Engineer @ Radical

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Problem: Every cancer patient is different but treatment isn’t data driven

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• There are thousands of potential treatment plans for a cancer patient. We don’t know what’s right for each individual so clinicians use a one size fits all approach.
• We have millions of data points on each individual patient. Doctors aren’t computers, they can’t computationally process it.
• Learning how to pick the right treatment plan for a patient will improve outcomes more than any new drug that will come to market (fun fact: the average new cancer drug only improves survival by 2.8 months)

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Solution: AI Cancer Doctor

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We’re building generalist medical models to make every cancer treatment decision at superhuman performance.

In the short term, here is an example clinical questions the model will tackle:

• What is the optimal first-line treatment for stage 4 colorectal or pancreatic cancer? Currently, there are two options. We believe we can extend life by 20% just by learning how to pick the right for a patient.