Senior Applied Machine Learning Scientist

Unlearn•San Francisco, CA

About The Position

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 iterate quickly. Unlearners are gritty ; we never give up, setbacks just make us try harder. Unlearners are receptive to new ideas ; in fact, we hate being stuck with the status quo Unlearners are storytellers ; sharing information with each other and with the world is super important, too important to be boring. And, last but not least, Unlearners are team-oriented ; we put the mission first, the company second, the team third, and individuals last. Headquartered in San Francisco, Unlearn was founded in 2017 by a team of world-class machine learning scientists. We have raised venture capital from top tier investors such as Altimeter, Insight Partners, Radical Ventures, 8VC, DCVC, and DCVC Bio, and completed our $50 million Series C in January 2024. If our purpose and culture resonate with you, we invite you to apply. Senior Applied ML Scientists lead Unlearn’s work to develop state-of-the-art ML approaches for generating Digital Twins – probabilistic models of a patient’s future health outcomes given knowledge of their current and past medical history. Senior Applied ML Scientists at Unlearn come from a wide range of disciplines, and have honed their ML expertise through their previous experience conducting novel and impactful research at top academic and industrial labs or their previous work delivering ML and data-science products in highly ambiguous and challenging commercial settings. Successful Applied ML Scientists at Unlearn are entrepreneurial in their approach; feeling a strong sense of end-to-end ownership of their mission, they investigate broadly to find the right tools and techniques to help their teams succeed. They are also highly determined individuals, powering through problems with cleverness and resolve.

Requirements

M.S. in computer science or engineering, physics, mathematics, or a related field.
3-4+ years of experience developing machine learning models and adapting them to solve real-world problems.
Previous experience with unsupervised ML, EBM, NLP, LLM, optimization theory, or reinforcement learning.
Strong software engineering skills and collaborative software development.
Fluency in the Python machine learning and data science ecosystem.
Evidence of successful execution of ML projects in an industrial setting.
Solid fundamentals in conceptual basics of ML architecture (linear algebra, statistics, optimization).

Nice To Haves

Contributions to well-known open-source ML tools or frameworks.
Prior experience working with healthcare or clinical machine learning applications.
Familiarity with AWS cloud computing services.

Responsibilities

Design and implement machine learning models to characterize and predict disease progression.
Apply and fine-tune proprietary architectures to real-world clinical data.
Clearly communicate technical findings and results to internal and external stakeholders.
Stay up to date with developments in the ML field to inform Unlearn’s modeling work.
Represent Unlearn to the broader scientific community.