Computational Pathology Scientist I—Imaging & ML

Broad Institute; Cambridge (MA); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Medical insurance

401(k) plan

Paid holidays and time off

Job summary

The Broad Institute of MIT and Harvard is seeking a highly motivated Computational Scientist I to join the Surfaceome Target Discovery team. You will develop and deploy quantitative image analysis workflows for whole-slide tissue images, focusing on IHC and mIF panels to drive surface target validation in tumor tissues.

Responsibilities include designing ML-based digital pathology methods, building scalable pipelines, and collaborating with pathologists and translational researchers to produce

Qualifications

• Master's or Ph.D. in Biomedical Engineering, Bioinformatics, CS, or related field with quantitative life sciences focus.
• 4+ years of relevant experience for Master's degree holders.
• Hands-on experience with QuPath and image analysis workflows.
• Proficiency in Python, R, and Groovy for analysis and automation.

Responsibilities

• Design, develop, and optimize ML-based digital pathology methods for segmentation, classification, and spatial analysis.
• Build scalable preprocessing, QC, and analysis pipelines for TMA and whole-slide imaging datasets.
• Validate imaging workflows and classifiers with benchmarking and external datasets.
• Collaborate with pathologists and scientists to assess quality, artifacts, and tissue integrity.
• Translate biological questions into quantitative imaging metrics and outputs.
• Coordinate analyses across cross-functional teams and maintain reproducible workflows.

Skills

Machine learning

Image analysis

Groovy scripting

Python

R

Team collaboration

Education

Master's or Ph.D. in Biomedical Engineering

Tools

QuPath

Akoya PhenoImager/Opal

VISIOPHARM

Docker

AWS/GCP

TensorFlow/PyTorch

Job description

The Broad Institute of MIT and Harvard is seeking a highly motivated Computational Scientist I to join the Surfaceome Target Discovery team. You will develop and deploy quantitative image analysis workflows for whole-slide tissue images, focusing on IHC and mIF panels to drive surface target validation in tumor tissues.

Responsibilities include designing ML-based digital pathology methods, building scalable pipelines, and collaborating with pathologists and translational researchers to produce