You will be joining the Computational Research and Engineering (CORE) team, which is responsible for R&D and deployment of the computational platform of the company. You will be working with top-tier engineers and scientists solving challenging genomics and AI/ML problems.
THE ROLE:
You will become an applied ML Engineer at Exai Bio. You will help lay the foundation for how we solve engineering problems and make ML models for applications in liquid biopsy and oncology.
How You'll Contribute:
Work with computational biologists and engineers to apply ML techniques to large-scale genomic data
Develop, train, and evaluate ML models
Deploy ML models in a production environment
Help define our software engineering culture
Our Ideal Candidate Will Have:
4+ years of industrial experience in production-level ML development
You’re passionate about using software and technology to help prevent diseases and applying your skills in the service of a greater mission
Knowledge and experience in applied ML research (e.g. model interpretability)
Experience in data security and privacy
You have acted as an engineering tech lead in building and maintaining ML pipelines
Maintain a high bar for code quality and are passionate about rigorous engineering practices
Experience with Kubernetes, MLFlow or Kubeflow or similar MLOps stack
Excellent communication skills and ability to work with technical and non-technical partners from other teams
Nice to Haves:
You’ve previously worked with large amounts of healthcare or genomic data
Experience in working in a fast-paced research-oriented team
A track record of open-source projects or publications in this domain
ABOUT:
Exai Bio Inc. (Exai) is a next-generation liquid biopsy company, bringing unprecedented insight into cancer biology from standard, non-invasive blood samples. Our company’s mission is to deliver the earliest and most accurate diagnosis of cancer. Exai is backed by several leading investors in cancer diagnostics and artificial intelligence/machine learning (AI/ML), including Section 32, Casdin Capital, and Two Sigma Ventures.