Applied Machine Learning Scientist

Unlearn•San Francisco, CA

1d•$140,000 - $165,000

About The Position

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.

Requirements

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.

Nice To Haves

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.