Scientist I, Machine Learning

About the Job:

The Scientist I, Machine Learning contributes to research, implementation, and validation of computational methods for FMI’s internal Computational Discovery Research group. This position supports the development of machine learning algorithms and data pipelines applied to large-scale biomedical datasets including histopathology imaging, genomic, transcriptomic, and clinical outcomes data. The Scientist I works closely with other machine learning scientists, computational biologists, clinicians and software engineers to contribute to workflows that may include the investigation and identification of novel biomarker signatures, discovery of novel cancer genomics findings, support of critical data science partnerships, and improvement to FMI’s operational pipelines.

Key Responsibilities:

• Develop, train, and evaluate machine learning and deep learning models using large-scale structured and unstructured datasets (images, free text) to extract biological insights
• Develop data pipelines, infrastructure, and computational tools for image or genomic analyses
• Contribute to data preparation, feature engineering, model validation and performance assessment
• Apply established machine learning and statistical methods with guidance from senior team members
• Provide scientific expertise and support for internal teams and external collaborators
• Conduct novel cancer genomics research using both public and internal datasets
• Collaborate with cross-functional teams including computational biology, pathology, and engineering 
• Implement reproducible analyses and contribute to codebases
• Prepare reports and presentations to communicate results in group meetings
• Present novel findings via abstracts or manuscripts
• Other duties as assigned
• Comply with FMI's attendance policies

Qualifications:

Basic Qualifications:

• Bachelor’s Degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or other similar quantitative discipline and 3+ years of work experience in relevant field; OR
• Master’s Degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or other similar quantitative discipline and 2+ years of experience in relevant field

Preferred Qualifications:

• Ph.D. degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or other similar quantitative discipline
• Experience with deep learning (particularly foundation models, vision transformers) methods and frameworks and a strong understanding of their mathematical foundations
• Knowledge of cancer biology and cancer genomics
• Intermediate proficiency or higher in object-oriented programming with Python, Java, or C++
• Experience with traditional machine learning methods and packages (e.g. sklearn) and a strong understanding of their mathematical foundations
• Experience with distributed processing and computation (Spark, Horovod, job scheduling, etc.) for large-scale datasets
• Experience working in shared code repositories using modern version control practices (e.g., Git, pull requests, code review)
• Familiarity with the ML development life cycle or MLOps
• Experience with histopathology analysis
• Familiarity with using cloud compute providers (AWS, GCP, etc.)
• Previous authorship/co-authorship of relevant work
• Strong communication and teamwork skills to work effectively in a flexible, cross-functional environment
• Understanding of HIPAA and the importance of patient data privacy
• Commitment to reflect FMI’s values: Integrity, Courage, and Passion

The expected salary range for this position based on the primary location of Boston, MA is $131,920 - $164,900 per year. The salary range is commensurate with Foundation Medicine’s compensation practice and considers factors including, but not limited to, education, training, experience, external market conditions, criticality of role, and internal equity. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for Foundation Medicine's benefits.

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