Unlearn
Applied Machine Learning Scientist
- Location
- San Francisco, CA, US
- Employment type
- Full-time
- Posted
- 17 August 2026 (about a month ago)
Checked 26 days agoApplications go to the employer, never to RoleSprint
About this role
OUR MISSION AND VISION
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.
ABOUT OUR TEAM
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.
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.
RESPONSIBILITIES INCLUDE:
- 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.
MINIMUM 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.
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.
BENEFITS & PERKS
The following benefits and perks are for full time roles only.
- Meaningful 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
Unlearn is an equal opportunity employer.
At Unlearn, we are committed to building a diverse and inclusive workplace, because inclusion and diversity are essential to achieving our mission. If you’re excited about this role, and your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply nevertheless.
Work location
- San Francisco, CA, US
Related jobs
Ready to make a decision?
This role is either worth your time or it isn’t.
Analyze the posting against your experience, see the gaps clearly, and build the right materials only if the opportunity makes sense.
Nothing is submitted automatically. You choose what happens next.
About this listing
Published on Ashby under the board identifier Unlearn, which is the name the employer’s own job board carries. RoleSprint has not verified the company’s registered or trading name, so it is shown exactly as published rather than tidied up.
RoleSprint is not the employer and not a recruiter. Applications are made on the employer’s own site and never reach us; what RoleSprint does is help you decide whether a role is worth your time and prepare for it if it is.
Published 17 August 2026, last checked 26 days ago. A posting stops being advertised here 90 days after the employer published it, and one the employer takes down is marked closed rather than quietly removed.