Use a modern tech stack to directly fight cancer, one patient at a time
Work in a collaborative interdisciplinary environment on multi-functional project teams of: software engineers, automation engineers, data scientists, machine learning experts, bioengineers, computational biologists, process engineers, immunologists, clinical and R&D scientists, etc.
Learn about the state of the art in cancer treatment with a team dedicated to both personal and scientific growth and development
Well funded with over $17M (series A) from Builders Venture Capital, Founders Fund, First Round Capital, Y Combinator, several prominent angels and seed-stage funds, and Accelerate Brain Cancer Cure, a venture philanthropy firm founded by Steve Case
Our engineering team consists of three functional area: software engineering, lab automation engineering, and bioinformatics. We work in two-week cycles that start with a planning meeting to discuss features/timelines and end with a demo/retrospective. Engineers work closely with both the PM and the science and business development teams to spec features. We use GitHub and pull requests for code reviews. Everyone is responsible for testing their own code and we use CircleCI for CI/CD which enables all engineers to push to production after review.
Oncology and immunology data is complex and the engineers at Notable Labs build data-rich web applications for scientists and laboratory users to visualize scientific data, talk to robots, drive laboratory workflows, organize medical knowledge, and facilitate cancer treatment discovery. To grow beyond the limits of manual data analysis we are actively applying machine learning techniques to the interpretation and review of this data. In addition to helping individual patients, we're creating novel datasets of drug and immune responses in primary samples that can be mined to discover new therapeutics and build decision support systems for clinicians.
Build a web application that allows scientists to create templated workflows that inferface with our robotic lab.
Build an automated PDF report of a patient's drug sensitivity compared to all the historical results in a disease indication for use in a clinical trial.
Create a machine learning pipeline to automate the interpretation of flow cytometry data.
Have compassion for patients above all else. Believe that all cancers can be treated. Celebrate life. Support and respect everyone. Work with passion. Act as a collaborative hub for our community. Make empirically data driven decisions. Be relentlessly resourceful. Foster a learning organization.
Interested in this company?
Skip straight to final-round interviews by applying through Triplebyte.