
Unlearn.AI uses machine learning to create digital twin patients that improve the design and analysis of randomized clinical trials. Their platform trains prognostic models on historical clinical…

Unlearn.AI uses machine learning to create digital twin patients that improve the design and analysis of randomized clinical trials. Their platform trains prognostic models on historical clinical…
What they do: B2B SaaS that builds prognostic 'digital twin' models to augment randomized clinical trials (TwinRCTs) and reduce sample sizes / timelines.
Founded: 2017
Headquarters / base: San Francisco, CA
Total disclosed funding: ≈ $134.85M (aggregate reported across profiles)
Key tech / methods: Machine learning, deep learning, biostatistics (digital-twin generation, trial simulation)
Clinical trial efficiency and design; reducing time, cost, and uncertainty in randomized controlled trials using historical data and predictive models.
2017
Research Services
12000000
Series A reported at $12M.
50000000
Series B reported at $50M.
Most recent funding activity date reported in aggregated profile data.
“Has raised institutional venture rounds with participation from investors such as 8VC, Insight Partners, DCVC / DCVC Bio, Radical Ventures, and Mubadala Capital Ventures.”
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We are advancing AI to power the future of medicine. 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.
Unlearn is a technology company, not a biotech company. We use computers, not pipettes. We make and use software, we don’t discover or make drugs. We believe that AI will define the future of medicine, and we aren’t deterred by naysayers or skeptics.
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
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 recently completed our $50 million Series C in January 2024.
If our purpose and culture resonate with you, we invite you to apply. The Role We are seeking a Finance Manager, FP&A who will play a critical role in supporting budgeting, forecasting, financial modeling, and operational reporting across the company. This is a highly hands-on role , and you should be excited to build models, analyze data, prepare reporting packages, and support decision-making across multiple teams.
You’ll report to our CFO and work closely with the Senior Finance team, Commercial, Product, and Operations stakeholders. The ideal candidate is analytical, detail-oriented, strong in Excel/Google Sheets, and eager to take ownership of recurring financial processes as well as ad hoc projects.
What You’ll Do Financial Planning & Analysis
Cross-Functional Support
Operational & Financial Reporting
Process Improvements & Systems
What You Bring Required Experience
Technical Skills
Personal Attributes
Benefits & Perks
The following benefits and perks are for full time roles only.
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.
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