Job description

OUR MISSION Every day, people are waiting for a diagnosis, for a treatment, for a chance. Our mission is to help life-saving treatments reach patients faster. That isn’t an abstraction to us. It’s the reason Unlearn exists, and it’s why the pace and rigor of our work matter so much. WHAT WE DO Unlearn is transforming clinical development by making every trial smarter. We partner with biopharmaceutical companies to redesign how clinical trials are planned, conducted, and analyzed. We use data, AI, and digital twins to make studies faster and more robust. A digital twin is a comprehensive prediction of a patient’s expected outcomes under a defined standard of care. It is generated by machine learning models built on our proprietary technology. Leading pharma and biotech companies trust us to deliver scientifically rigorous work on studies where the stakes are high and the timelines impact patients. WHERE WE’RE GOING Our goal is to become the gold standard for AI-driven clinical development. Getting there takes disciplined execution and data, models, and AI that is implemented with partners using a strong engineering foundation that holds up in a regulated, compliance-heavy environment. We’re building durable advantages rooted in science and innovative technologies, and that foundation is what will make our leadership last. OUR VALUES Accountability, Agility, and Acceleration describe how we work with each other every day. Fulfilling our mission depends on all of us doing our best work, and it’s on all of us to build and protect the culture that makes that possible. Accountability: We own outcomes, not just tasks We take responsibility for results, make decisions with urgency, and follow through on our commitments. When things don’t go as planned, we say so. We surface issues early, learn, iterate quickly, and move forward without blame. We give and receive feedback that is clear, direct, and actionable. Agility: We follow the evidence Drug development and AI evolve rapidly. We stay curious, embrace new information, and change course when the evidence changes. We keep a close eye on where the industry is heading and build technology flexible enough to adapt when new data or opportunities arise. We value speed of learning over being right. Acceleration: We create momentum Our mission is to accelerate the development of new medicines, and that starts with how we work. We simplify, prioritize, and execute with urgency. Unlearners own the problems in front of them and are empowered to solve them. That helps our customers, our teammates, and our company move faster without sacrificing scientific rigor. ABOUT UNLEARN Unlearn was founded in 2017 by a team of world-class machine learning scientists and is headquartered in San Francisco. We’re backed by top-tier investors including Altimeter, Insight Partners, Radical Ventures, 8VC, and DCVC. We have substantially advanced AI technology for clinical trials, including regulatory-grade applications, and are embedded in multiple clinical trials with top Pharma and Biotech companies. ABOUT THE ROLE Our models are only as good as the data they are built on. This role owns the data products that make those models possible: the pipelines, datasets, and tooling that turn raw clinical trial data into something our scientists can train and deploy models with. You will work day to day with the engineers who build that infrastructure and with the teams who build and deliver models for our customers. Your job is to understand how those teams actually work, decide what gets built next, and make sure it ships and gets used. This is a hands-on product role on a foundational data platform, which means the problems worth solving usually surface in conversation rather than in a backlog. You will spend real time alongside the people doing the work, and in the data, figuring out together what matters most. It suits someone technical enough to be a genuine participant in an engineering design discussion, comfortable enough with data to explore questions themselves, and able to make a clear call about what not to build when the team needs one. You will own this area end to end, working closely with the Head of Product, who sets overall direction with you, and with the engineering and scientific leads whose teams depend on your work. YOUR FIRST 90 DAYS We would rather tell you what success looks like than list everything you might touch. By the end of your first quarter, we expect you to have: - Built a clear, evidence-based picture of how our data supports model development. Mapped how data moves from raw clinical trial data to the inputs our models require, found where the friction is, and turned that into a prioritized recommendation for what we fix first. - Shipped improvements that measurably speed up model delivery. Owned the roadmap for the data products our internal model-building teams depend on, grounded in how they actually work rather than how the specs say they should, and gotten at least one meaningful improvement into production. Success is measured in how much faster models get built, not in features shipped. - Established a prioritization rhythm your partner teams trust. Set up a working way for requests to reach you, get sequenced, and get communicated back, so the engineers building the platform and the teams using it know what is coming and when. WHAT YOU'LL DO - Develop a first-hand understanding of how our scientific and engineering teams build models, working with the data directly to see where time is being lost. - Translate what you learn into prioritized work: clear problem statements and requirements engineers can build from, with the tradeoffs made explicit when scope has to give. - Partner with engineering leads on technical design decisions, bringing a product and user perspective to architecture choices without trying to make them for the team. - Define how success is measured for your area, instrument it, and report on it honestly, including when the numbers are not flattering. WHAT WE'RE LOOKING FOR - At least 3 years as a product manager on technical products, with meaningful time spent on data platforms, developer tools, or internal infrastructure, and a track record of owning an area end to end and shipping things people actually adopted. - Real technical depth: you can read a schema, write your own queries, reason about how a pipeline is put together, and hold a credible conversation about a design tradeoff with the engineers building it. Messy, high-volume, highly structured data does not put you off, and you explore it yourself before forming a view. - Clear written thinking. You can lay out a problem, the options, and your recommendation in a short document that a technical audience will take seriously. - The judgment to work independently inside a scope we define together, and to recognize when a decision is better made with others than alone. - AI-native in how you work, with these tools built into your daily workflow. NICE TO HAVE - Experience with clinical trial data or clinical data standards. - Experience in the biopharma industry or in clinical development. - Background supporting machine learning teams, particularly on the data side of model development. - Experience turning internal tooling into something that could be sold. - Experience with Databricks. If your experience doesn't line up with every point above but you think you can do this job, tell us why. BENEFITS & PERKS The following benefits and perks are for full time roles only. - Meaningful equity participation - 100% company-covered medical, dental, & vision insurance plans for you and your dependents - 401(k) plan with matching - Flexible PTO plus company holidays - Annual company-wide break December 24 through January 1 - Commuter and Wellness benefits - Tech stipend - 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.

Requirements

Must-haves

We would rather tell you what success looks like than list everything you might touch. By the end of your first quarter, we expect you to have:
Develop a first-hand understanding of how our scientific and engineering teams build models, working with the data directly to see where time is being lost.
Translate what you learn into prioritized work: clear problem statements and requirements engineers can build from, with the tradeoffs made explicit when scope has to give.
Partner with engineering leads on technical design decisions, bringing a product and user perspective to architecture choices without trying to make them for the team.
Define how success is measured for your area, instrument it, and report on it honestly, including when the numbers are not flattering.
Clear written thinking. You can lay out a problem, the options, and your recommendation in a short document that a technical audience will take seriously.
The judgment to work independently inside a scope we define together, and to recognize when a decision is better made with others than alone.
AI-native in how you work, with these tools built into your daily workflow.

Nice-to-haves

Experience with clinical trial data or clinical data standards.
Experience in the biopharma industry or in clinical development.
Background supporting machine learning teams, particularly on the data side of model development.
Experience turning internal tooling into something that could be sold.
Experience with Databricks.
If your experience doesn't line up with every point above but you think you can do this job, tell us why.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learninggo

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave

Wellness