Uppercopy
Mini tools
At Unlearn, our purpose is to advance artificial intelligence (AI) to eliminate trial and error in medicine. We are innovating advanced machine learning methods to leverage generative AI in forecasting patient outcomes, starting with the domain of clinical trials. We produce AI-generated digital twins of individual trial participants, enabling smaller and more efficient clinical trials to bring effective medicines to patients sooner.
Our innovative work in AI today will reinvent how AI is applied in medicine tomorrow — and we have a top secret plan for how to get there. We won’t be able to achieve this mission just by applying technologies created by others; the future must be invented.
Applied Machine Learning Scientist
Unlearn
On-site
San Francisco, CA, United States
Full-time
$140,000 -
$165,000
About Unlearn
About the Role
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. 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.
Qualifications
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.
Bonus points for:
Contributions to well-known open-source ML tools or frameworks.
Previous experience with unsupervised ML, EBM, NLP, LLM, optimization theory, or reinforcement learning.
Prior experience working with healthcare or clinical machine learning applications.
Familiarity with AWS cloud computing services.
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.
Bonus points for:
Contributions to well-known open-source ML tools or frameworks.
Previous experience with unsupervised ML, EBM, NLP, LLM, optimization theory, or reinforcement learning.
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.
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.
Benefits
Generous equity participation.
100% company-covered medical, dental, & vision insurance plans.
401k plan with matching.
Flexible PTO plus company holidays.
Annual company-wide break December 24 through January 1.
Commuter benefits.
Paid Parental Leave.
Support for H1B, TN, and E-3 Visa change of employer transfers.
100% company-covered medical, dental, & vision insurance plans.
401k plan with matching.
Flexible PTO plus company holidays.
Annual company-wide break December 24 through January 1.
Commuter benefits.
Paid Parental Leave.
Support for H1B, TN, and E-3 Visa change of employer transfers.
