Principal Machine Learning Scientist Gritstone Oncology
Cambridge, MA

Job Description

Gritstone Oncology is a fast-growing, innovative biotechnology company developing the next generation of personalized cancer immunotherapies. Gritstone brings together distinguished scientific founders, an experienced and diverse management team, a seasoned and successful board of directors, and very deep financial backing to tackle fundamental challenges at the intersection of cancer biology, immunology and immunotherapy design. The goal is to develop breakthrough immunotherapy treatments for patients with cancer by identifying and deploying neoantigens from each patient's tumor to develop personalized cancer vaccines. Gritstone Oncology is headquartered in the San Francisco Bay Area with certain key functions located in Pleasanton, CA and Cambridge, MA.

Gritstone Oncology is seeking a Principal Machine Learning Scientist to help harness the immune system to treat cancer. Gritstone is a fast-growing, innovative biotechnology company developing the next generation of personalized cancer immunotherapies. The Company's initial goal is to identify and deploy neoantigens for novel treatments for lung, gastric, and colorectal cancer. In addition to neoantigen therapy development, Gritstone is leveraging its antigen discovery platform EDGETM to define targets for shared antigen immunotherapies, which would provide an opportunity to treat even more patients.

Gritstone has already made significant advances in the application of machine learning to the identification of tumor antigens, as recently described in our article in Nature Biotechnology (https://www.nature.com/articles/nbt.4313). We are now seeking a leader to build on, extend, and innovate beyond this first step in the service of creating powerful immunotherapies for cancer patients. This position offers significant responsibility and visibility within and outside the organization and reports to the Chief Technology Officer.

Responsibilities:

* Leader in designing and optimizing large-scale machine learning models for applications in cancer immunotherapy.
* Collaborate with proteomics and genomics lab scientists to design and implement high-throughput training data generation strategies
* Maintain and improve existing machine learning models that determine the composition of personalized cancer immunotherapies
* Devise and conduct machine learning experiments to investigate and validate new model structures, features, and methods on industry-leading proprietary training data sets
* Supervise junior machine learning scientists and engineers

Requirements:

* Undergrad and PhD in quantitative disciplines (e.g., computer science, math, physics)
* 5+ years total working experience with 3+ years in machine learning applied to the biology domain
* Proficiency in the Python programming language and ML stack
* Proficiency in conducting machine learning/statistical experiments and manipulating data sets
* Strong command of computational biology and genomics
* Deep understanding of statistical and optimization techniques
* Clear track record of machine learning related publications/presentations

Desired qualifications:

* Software engineering experience, including full time industry experience or academia
* Application of neural networks to solve complex biological problems
* Project/Team leadership experience

Proficiency in related software packages

* Data manipulation, e.g., NumPy, SciPy, pandas
* Machine learning, e.g., scikit-learn, TensorFlow, Keras
* Data visualization, e.g., Matplotlib, Seaborn