We’re building a platform powered by machine learning to understand the biology of aging and power the discovery of therapeutics.
Aging is the single greatest risk factor for the most detrimental diseases on Earth — cardiovascular disease, neurodegenerative disease, pulmonary disease, cancer, muscle wasting, and more — and drugs that slow the biological damage accumulated while aging have the potential to reduce the incidences of these diseases, possibly simultaneously. We believe that in the not-too-distant future, the discovery of therapies for aging will provide some of the most effective tools in history for reducing our burden of disease and extending our healthy lifespan.
Our mission is to dramatically accelerate the realization of that future.
Our early team brings experience from Google, Khan Academy, FogCreek, CZI, and leading research labs in the field of aging. Additionally, we've got advisors who are world leaders in aging science, senior execs from pharma, and top tech entrepreneurs.
We have deep support from some of the best investors in the world: General Catalyst, First Round, Felicis, Laura Deming's Longevity Fund, Sam Altman, and more. We recently just raised another $18M in our Series A to continue the momentum we've created in our early research programs (more here: https://medium.com/spring-discovery/with-18-million-in-new-funding-spring-is-speeding-up-our-engine-for-discovering-aging-therapies-c2ea6ff7d330)
In less than year, we've established the beginning of a data pipeline bringing in >1Tb of rich biological data every week, built the beginnings of a flexible and scalable (100's of GPUs on demand) machine learning platform, and made significant research progress and findings in multiple programs.
This is a unique moment: the confluence of recent scientific evidence showing that aging is malleable, the emergence of powerful machine learning techniques, and the world-class team and support that has come together puts us at the forefront of a new age of biotech.
Our engineering work falls into three broad buckets: 1) analyzing data from our experiments, 2) researching new computational approaches, and 3) building new technical infrastructure for our lab and our platform. We're very goals driven, setting goals at the 3-month, 1-month, and 1-week level, and we meet weekly to review/grade our goals. While we encourage clear accountability and ownership, we don't have strict
roles and expect team members to be able to wear different hats and do a variety of different kinds of work in service of our mission. We also have very collaborative and cross-functional teams; our engineers work closely with our biologists to help design new experiments, analyze and troubleshoot data, and teach each other about biology and computer science.
We're building a platform to analyze vast amounts of rich biological data. This involves exploring, designing, and training machine learning models using more traditional as well as newer deep learning techniques. But machine learning is just one tool; it alone is not sufficient. Our work also involves thinking of, designing, and building many other workflow and visualization tools to
give humans superpowers – we want to empower our scientists and automate their workflows and enable them to analyze data at consumer internet scale.
You'd leverage our platform to design, test, and refine new models to extract biological signals and answer new research questions from our experiments.
You'd help build and scale out our existing machine learning infrastructure to support new models and use cases, data types, and improve the velocity with which we conduct our research
You'd create data visualization reports and utilities to make sense of vast amounts of data and report on the results of our models.
We value speed of execution, and, more importantly, speed of learning. We set ambitious goals, and intend to make progress every single week towards it. We know that means working hard, but healthily so – this is an epic mission with a long road ahead and we're just getting started.
A critical aspect of our operation is deep collaboration between scientific and computational teams. We don't expect engineers to come with a background in biology, just as we don't expect our biologists to be experts in software engineering. We do expect our teams to work respectfully and closely, learning together every day.
We have daily standups at 10am and most people are active between 10am and 5pm, but generally trust our teammates to do what's needed to get things done at work, and in their own lives.
Learning is a big part of our culture and we're to committed to investing in our teammates. If you see a conference that can help you, we encourage you to attend, and will reimburse as appropriate.
We provide excellent medical, dental, and vision coverage.
Interested in this company?
Skip straight to final-round interviews by applying through Triplebyte.