Convert vaguely described biological/drug discovery challenges into well-defined machine learning problems, particularly in the Computational Pathology domain.
Independently execute and deliver full AI/ML-driven solutions from sourcing training data, designing and implementing SOTA machine learning models, testing, benchmarking, and product-driven research for model performance improvement, to shipping stable, tested, performant code and services in an agile environment
Lead and ensure the best machine learning practice
Work as part of a highly interdisciplinary team
Requirements & Skills:
A degree in a quantitative or engineering discipline (e.g., computer science, computational biology, bioinformatics, engineering, among others); OR equivalent work experience as a professional AI/ML engineer.
Highly experienced in developing deep learning models for solving real-world scientific problems
An outstanding scientist, machine learning engineer, and software engineer. Demonstrate expertise and depth in at least one area and breadth across your expertise.
Expertise and depth in deep learning for computer vision, including but not limited to image segmentation, object detection, weakly supervised learning, and self-supervised learning
Proficiency with standard deep learning algorithms and model architectures
Familiarity with current deep learning literature and math of machine learning
In-depth knowledge of machine learning best practices, scalable training and deployment, model introspection, and evaluation
Advanced level in PyTorch, Tensorflow, or other deep learning frameworks
Experienced/accomplished in software engineering with advanced skills in Python and/or C++
Experience with devop stacks: version control, CI/CD, containerization, etc.
A PhD in modern deep learning
Peer-reviewed publications in major AI conferences
Experience in the design, development, and deployment of commercial AI/ML software.
Track record of contributing to open-source projects
The mentality of committing early and often, metrics before models, and shipping high-quality production code
Knowledge in disease biology, molecular biology, and biochemistry
Experience with biological data (e.g., genomics, transcriptomics, epigenomics, proteomics, etc.), clinical data (e.g., electronic health records, clinical images, histopathology images)
Experience in working with large-size images at scale, e.g. histopathology images and their format.
