Full-Time

Principal Product Manager

Posted on 9/11/2025

Unlearn AI

Unlearn AI

51-200 employees

AI-powered patient digital twins for simulations

Compensation Overview

$230k - $280k/yr

H1B Sponsorship Available

San Francisco, CA, USA

Remote

Category
Product (1)
Required Skills
Product Management
Data Analysis
Requirements
  • At least 8 years experience as a product manager in a technology company working on technology-powered products as a product manager
  • Bachelor’s degree or equivalent practical experience with demonstrated knowledge of the clinical development space
  • Demonstrated ability to ship high-impact products quickly, not just small features
  • Demonstrated ability to figure out solutions to hard problems with many constraints, using sound judgment to assess risks, and to lay out your argument in a well-structured, data-informed, written narrative.
  • Demonstrated ability to learn and partner with multiple functional areas of business – engineering, design, finance, sales, and marketing.
  • Strong collaboration in team environments but able to act quickly as the decision-maker
  • Technical understanding must go from the highest abstractions down to the metal
  • You find a way to get the data you need and whip it into an insightful story with no help
  • An owner’s mindset - you don’t shy away from the hard stuff
  • Passion for our mission
  • Experience in biopharma industry or in clinical development
Responsibilities
  • You'll be responsible for framing and executing the product strategy, monitoring and communicating progress, and defining functional requirements for new product experiences and features
  • Perform quantitative and qualitative research to identify product opportunities and prioritize features
  • Partner with engineering, UXR, design, and analytics teams to define, build, and iterate on new user experiences
  • Developing hypotheses for customer and uses cases, and rapidly iterating through hypotheses to find product market fit for various use cases quickly
  • Responsible for assisting with quarterly planning and assigning objectives (problems to solve) to product teams.
Desired Qualifications
  • Experience building and pricing platform/tools

Unlearn.AI builds AI-powered digital twins of patients to simulate health outcomes and predict how conditions may evolve. It generates patient-specific virtual replicas from data and runs treatment scenarios to forecast results. Unlike traditional trials, its twins model control groups for clinical studies, potentially reducing the number of real patients needed and speeding drug development. The goal is to speed healthcare decisions, improve trial efficiency, and lower costs by using digital twin simulations to forecast outcomes.

Company Size

51-200

Company Stage

Series C

Total Funding

$130.7M

Headquarters

San Francisco, California

Founded

2017

Simplify Jobs

Simplify's Take

What believers are saying

  • AD DTG 4.2 released March 13, 2026, predicts p-tau217 outcomes using enriched biomarkers.
  • Partnerships with SOLA Biosciences for ALS SOL-257 trial and CHDI for Huntington's Enroll-HD.
  • TrialPioneer launched integrates Scout, Hindsight, SimLab to accelerate trial planning.

What critics are saying

  • FDA rejects p-tau217 biomarkers as endpoints, eliminating AD DTG differentiator in 12-24 months.
  • Eli Lilly, Roche internalize AI twins, commoditizing Unlearn's subscription in 18-36 months.
  • Parexel, IQVIA integrate simulations with pharma relationships, eroding Unlearn in 12-24 months.

What makes Unlearn AI unique

  • Unlearn invented TwinRCT using digital twins to reduce control arms by 33% while preserving power.
  • TwinRCTs create prognostic digital twins from baseline data for precise treatment effect estimation.
  • EMA-qualified and FDA-supported digital twins enable smaller RCTs with historical data integration.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Retirement Plan

401(k) Company Match

Unlimited Paid Time Off

Paid Holidays

Commuter Benefits

Paid Parental Leave

Company Equity

Growth & Insights and Company News

Headcount

6 month growth

-4%

1 year growth

-5%

2 year growth

-2%
Unlearn.ai
Mar 13th, 2026
AD model update: AD DTG 4.2 - exploring biomarker outcomes in Alzheimer's disease.

AD model update: AD DTG 4.2 - exploring biomarker outcomes in Alzheimer's disease. March 13, 2026. Unlearn is excited to announce the release of AD DTG 4.2, the latest update to its Alzheimer's disease Digital Twin Generator. This release expands the biomarker data available to its model and enables early exploration of biomarkers as potential clinical outcomes. Why biomarkers matter in AD trials. Biomarkers are measurable biological molecules whose concentration shifts in response to disease processes or treatments. Unlike traditional lab values that monitor general physiology, such as electrolytes or liver enzymes, disease-specific biomarkers target molecular pathways tied to a specific condition. In Alzheimer's, novel biomarkers like phosphorylated tau-217 (p-tau217), amyloid beta 40 (Aβ40), and amyloid beta 42 (Aβ42) can signal neurodegenerative changes years before symptoms appear, making them valuable for detecting disease, tracking progression, and evaluating therapeutic response in clinical trials. Expanded data, expanded possibilities. The updated data asset now includes or enriches several AD-relevant biomarkers, including: Aβ40, Aβ42, total tau (t-tau), phosphorylated tau-181 (p-tau181), p-tau217, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Incorporating these biomarkers into the data asset creates new opportunities to explore their role in inputs, outputs, or both when generating digital twins of study patients in future DTG releases. An early exploration of ptau-217 as a clinical outcome. AD DTG 4.2 incorporates two biomarkers from the updated data asset, p-tau217 and GFAP. P-tau217 is a sensitive and specific marker of Alzheimer's pathology, correlating with amyloid accumulation, tau burden, brain atrophy, and physical degradation - and notably does not predict such changes in patients with other neurodegenerative disorders, making it highly specific to Alzheimer's disease. This specificity makes it a compelling feature for modeling disease progression. With this release, the model can now predict longitudinal changes in ptau-217 concentration as a clinical outcome, a capability Unlearn is optimistic about as Unlearn continue to grow and validate the underlying dataset. This capability may help support broader biomarker modeling within the AD DTG framework for future model updates. In addition, treating ptau-217 as the sole input to the model can provide prognostic information for other outcomes commonly measured in Alzheimer's trials. What's next. This biomarker work builds on the validated foundation of the AD DTG, which has already demonstrated meaningful impact in Alzheimer's studies. In retrospective analyses, digital twins of study participants have supported sample size reductions of up to 15% and up to 33% in control arms using Unlearn's EMA-qualified and FDA-supported method. As Unlearn continue to grow and enrich its AD dataset, now trained on over 25,000 patient records spanning cognitively normal individuals through moderate Alzheimer's disease, the model will be positioned to support increasingly robust biomarker modeling and stronger digital wins in future DTG releases. Blog.

The Associated Press
Mar 10th, 2026
Unlearn deploys AI digital twins to strengthen SOLA Biosciences' Phase 1/2 ALS gene therapy study

Unlearn, a San Francisco-based AI company, will support SOLA Biosciences' Phase 1/2 clinical study of SOL-257, an investigational gene therapy for amyotrophic lateral sclerosis. The partnership will use AI-generated digital twins to strengthen the early-phase study. The single-arm trial will use digital twins as external comparators to help interpret clinical outcomes, addressing challenges posed by disease heterogeneity and small sample sizes in early-stage ALS studies. Unlearn's digital twins are created by a machine-learning model trained on extensive patient-level historical ALS data. The collaboration will support trial planning, regulatory engagement and participant-level analyses during the Phase 1/2 study and long-term follow-up. Unlearn has secured EMA qualification and FDA support for its digital twin technology in clinical trials.

The Associated Press
Feb 24th, 2026
Unlearn refines AI model for Huntington's disease trials using CHDI Foundation data

Unlearn, an AI company specialising in clinical development, will use data from CHDI Foundation's Enroll-HD research platform to refine its Huntington's disease-specific Digital Twin Generator. The machine learning model generates individualised forecasts of disease progression using longitudinal, patient-level data. Enroll-HD is a global clinical research platform with over 22,000 active participants across 157 sites in 23 countries. CHDI Foundation is a privately funded nonprofit exclusively dedicated to developing Huntington's disease therapeutics. Unlearn's AI-generated digital twins are used in clinical trials to reduce variability and strengthen treatment effect estimation. The company has received EMA qualification and FDA support for applying AI in clinical trials. The collaboration aims to improve how Huntington's disease trials are designed and analysed.

The Associated Press
Jan 28th, 2026
Unlearn launches TrialPioneer AI workspace to accelerate clinical trial planning

Unlearn, a leader in AI solutions for clinical development, has launched TrialPioneer, an AI-powered workspace designed to accelerate decision-making in upstream trial planning. The platform helps clinical development teams optimise study designs by consolidating evidence, assumptions and scenario evaluations in one workflow. TrialPioneer integrates three capabilities: Scout for AI-powered precedent review from sources like PubMed and ClinicalTrials.gov; Hindsight for exploring historical benchmarks using patient-level data; and SimLab for on-demand trial simulations comparing design scenarios. The workspace addresses fragmentation in planning workflows by making assumptions explicit and traceable, enabling teams to evaluate trade-offs earlier and align on study designs. Unlearn has received EMA qualification and FDA support for its science-first approach to applying AI in clinical trials.

Yahoo Finance
Sep 25th, 2025
Dr. Robert Lenz Joins Unlearn as Strategic Advisor

Dr. Robert Lenz joins Unlearn as strategic advisor.

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