PicnicHealth

Own your health. Advance medicine.

Sf, remote

251 - 500 employees

101 - 250 engineers

$50m - $100m funding

Series b

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PicnicHealth is all about making medical records easy to access and using data to power the next frontier of medical research. We’re led by the belief that there’s power in medical information when it’s used well, and that every person has a right to their own theirs. And we've built award-winning machine learning to do it.

PicnicHealth works directly with patients to give them the only tool anywhere that truly provides all of their medical records from any doctor or healthcare system in the US—in an easy-to-use, secure online dashboard. Not only that, but patients can choose to contribute their data to scientific research, which helps life sciences researchers accelerate breakthroughs in care. Researchers use this anonymized “real-world data” to understand how diseases are experienced by patients and treated by clinicians in the real world, going beyond the highly controlled setting of clinical trials. The goal is to create better treatments that can ultimately improve outcomes for patients.

Whether you’re a scientist, engineer, or patient yourself, we’re looking for passionate people to join us in redefining an industry during our next stage of growth. To date we’ve helped tens of thousands of patients, partnered with some of the world’s largest biopharma companies, and raised more than $35M from Felicis Ventures, Amplify Partners, and Y Combinator.

Active Roles

Data Engineer San Francisco, CA, United States or Remote

Front End Engineer San Francisco, CA, United States

Full-Stack Engineer San Francisco, CA, United States or Remote

Infrastructure Engineer San Francisco, CA, United States or Remote

Why join us?

We have the tools to do something new — The world is in the middle of a revolution in medicine — you might say that data is eating medicine 😉. In the traditional healthcare model, controlled studies were the only way to understand what was going on in the world, and there was a one-size-fits-all approach for applying those study results to patient care. Now, there are huge new data sets (genomics, imaging, sensors, electronic medical records), and thanks to ML, people can use that data to understand healthcare. Further, as there is more data on each patient, care can get more personalized. Breast cancer used to be a single disease, and now it’s dozens of different diseases with different molecular properties, each of which imply different treatment paths.

All of these new approaches to medicine require medical outcomes data. Billions of dollars are being poured into creating data sets by everyone from the big tech players to small biotechs. But medical outcomes data lives in medical records and those records are incredibly hard to get. Our strategy of working directly with patients and getting their consent to contribute data to specific studies makes us the only company that can get complete, structured outcomes data from any healthcare provider at scale, today.

Our initial market consenting patients to contribute outcomes and training data for life sciences companies is a billion dollar market on its own. And that’s just getting started.
By working on PicnicHealth you’ll be building something that literally saves our users’ lives. You can read some of their stories on our blog: https://blog.picnichealth.com/tag/patient-stories/.

Beyond empowering patients and improving their care, the world desperately needs to solve the problem of fragmented, incomplete, unstructured medical data. Empowering medical researchers to access the data they need will unleash new techniques, therapies, and diagnostics that will drive healthcare forward.
We believe that engineers should own what they’re building. The people building a system are in the best place to decide how to move it forward, so our engineers work with operations, sales, users, and customer success to drive and define the product roadmap.

Engineering at PicnicHealth

Technical Challenges

1) We’ve already created a significant training set of annotated records, and have a record processing pipeline for annotating any new records that come in. We need to improve our ML models to improve automated structuring and drive our process to be as efficient as possible.

2) We create large amounts of structured medical information for our users and need to enable them to surface relevant information quickly and easily. This means performant systems for searching for (potentially poorly specified) information, and effective ways of interacting with and visualizing that information.

3) We’re on track to create the world’s largest, most complete, and best structured dataset of medical histories and outcomes. We need to massively scale our service and systems to execute on this opportunity.

Tech stack

Node

PostgreSQL

Redis

Google Cloud

Redux

Kubernetes

Docker

Python

Keras

TensorFlow

ElasticSearch