The Machine Learning Engineer will participate in research and development efforts aimed at solving problems in analyzing large-scale clinical data with a mission of improving human health. The candidate will work with multiple modalities including electrocardiogram, echocardiogram, and MRI data. The candidate will also develop methods to ascertain disease outcomes as well as characterize risk factors from large electronic health record data sets. Rich representations of clinical data derived from deep learning models will be used in conjunction with genetic data to investigate the genetic basis for disease. The ideal candidate has both a practical understanding of deep learning techniques and has experience in areas such as clinical research, probability, statistics, or data engineering.
The candidate joins a strong team of machine learning practitioners to work with, has access to vast amounts of clinical data, and is encouraged to publish new methods and results in academic journals and conferences. The candidate will conduct research in clinical ML and disease biology, and must collaborate effectively with researchers at the Broad Institute and beyond. This position is suited to a person who is excited by the prospect of learning, adapting and applying modern machine learning techniques to solve the key challenges for emerging clinical data modalities, with revolutionary implications in advancing the state-of-the-art clinical practice.
- Adapting and applying existing machine learning techniques to clinical datasets
- Developing novel machine learning methods for understanding and organizing unstructured datasets
- Developing robust and generalizable inference algorithms that advance the state-of-the-art
- Writing well-crafted, maintainable, scalable, and performant machine learning code
- Designing, developing, and maintaining testing frameworks for machine learning code
- Developing techniques for characterizing, processing, and storing large real world clinical datasets
- Bachelor’s degree in Computational Biology, Computer Science, Physics, Math, Statistics, or related quantitative fields
- Strong communication skills and ability to collaborate with clinicians, data scientists, and software engineers on model requirements and design
- Practical experience with python-based deep learning frameworks (e.g. TensorFlow or PyTorch)
- Strong bash/shell scripting and proficiency with UNIX operating systems
- Familiarity with Numpy and Pandas
- Knowledge of software engineering best practices including version control and writing tests
- Experience developing data pipelines to prepare data for modeling from large, messy data sets
- Experience working with clinical imaging data or omics data
- Ph.D. in Computational Biology, Computer Science, Physics, Math, Statistics, or related quantitative fields
All Broad employees, regardless of work location, must be fully vaccinated for COVID-19 by Tuesday, October 12, 2021. Requests for exemption for medical or sincerely held religious beliefs will be considered.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
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