Machine Learning Scientist - Biomedical Imaging & Genomics

Foundation Medicine; Boston (MA); Hybrid; USD 132,000 - 165,000; Full time

Benefits offered by this job

Benefits

Bonus potential

Hybrid work

Job summary

Foundation Medicine in Boston seeks a Scientist I, Machine Learning to contribute 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.

Qualifications

• Bachelor’s degree in Computer Science, Bioinformatics, Computational Biology, Engineering, or related quantitative field with 3+ years of experience, OR Master’s degree with 2+ years of experience.
• PhD preferred with strong background in computer science, bioinformatics, or related field.
• Experience with deep learning foundations and frameworks (vision transformers, CNNs) and a solid math background.
• Proficiency in Python, Java, or C++, and familiarity with Git and modern software practices.

Responsibilities

• Develop, train, and evaluate ML and deep learning models on large, structured and unstructured data (images, text).
• Build data pipelines and tools for image and genomic analyses.
• Contribute to data preparation, feature engineering, validation, and model performance assessment.
• Collaborate with computational biology, pathology, and engineering teams.
• Prepare reports and communicate results to internal and external collaborators.

Skills

Python

Java

C++

Deep learning

Spark

Git

MLOps

Education

Bachelor’s degree

Master’s degree

PhD

Tools

TensorFlow

PyTorch

Horovod

Spark

AWS

GCP

Job description

Foundation Medicine in Boston seeks a Scientist I, Machine Learning to contribute 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.