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.

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 an internal 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

• Own the roadmap for an internal data platform area end to end, from problem definition through delivery and adoption.

• Develop a first-hand understanding of how our scientific and engineering teams build models, and translate what you learn into prioritized, well-specified work.

• Write clear problem statements and requirements that engineers can build from, and make the tradeoffs 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.

• Run the planning and prioritization rhythm for your area, and keep partner teams informed about what is coming and when.

• Work with the data directly to answer questions about usage, quality, and where time is being lost.

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.

• A track record of owning a product area end to end and shipping things people actually adopted, not just features that were delivered.

• 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.

• Comfort working with messy, high-volume, highly structured data, and the habit of exploring 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.

• Someone who is AI native and can incorporate it into your daily workflow.

• An owner's mindset. You don't shy away from the hard stuff.

• Passion for our mission.

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. We would rather read that than a perfectly matched resume.

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.