Discover how Unlearn uses AI to create digital twins of clinical trial participants, streamlining clinical development and enabling confident decision-making. Backed by Insight.
About the role
Design and implement machine learning models to characterize and predict disease progression. Apply and fine-tune proprietary architectures to real-world clinical data.
What they're looking for
- B.S. in computer science or engineering, physics, mathematics, or a related field
- 2+ years of experience developing machine learning models and adapting them to solve real-world problems
- Demonstrable competency in the fundamentals of software engineering
- Fluency in the Python machine learning and data science ecosystem
- Evidence of successful execution of ML projects in an academic or industrial setting
- A track record of intellectual curiosity - e.g., exploring new techniques, tools, or ideas independently
More about this role
Unlearn exists to transform clinical development by making every trial smarter. We harness data, AI, and digital twins to enable faster, more robust studies that bring life-saving treatments to patients faster. This mission drives everything we do as we partner with biopharmaceutical companies to redesign how clinical trials are planned, run, and analyzed.
We are defining the future of clinical development with unmatched scientific credibility, replacing uncertainty with AI-powered precision so decisions are clearer and trials are stronger. We don’t just disrupt the pharmaceutical industry, we create lasting change.
We believe AI will define the future of medicine, and we are committed to building that future responsibly, rigorously, and in close collaboration with our partners in clinical development.
We come from a variety of backgrounds ranging from machine learning to marketing—but regardless of where we come from, Unlearners share some common traits:
Unlearners are ambitious ; we aren’t intimidated by big, challenging goals.
Unlearners are disciplined experimenters ; we break down our big goals into smaller chunks and meet as often as necessary to track our velocity and...
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