Job Description

Job Summary

The

University Health Network (UHN)

is seeking a highly motivated

Deep Learning Engineer

to join our Computational Pathology and Biomarker Discovery research program. The successful candidate will focus on

deploying and optimizing deep learning algorithms

for quantitative biomarker assessment from cloud-hosted whole-slide histopathology images.

You will collaborate closely with pathologists, biomedical researchers, and AI scientists to

design, implement, and deploy

scalable deep learning pipelines that enable high-throughput, reproducible biomarker quantification. This role is central to advancing precision oncology and translational research efforts at UHN.

Duties

Develop, optimize, and

deploy deep learning models

for digital pathology image analysis (e.g., cell segmentation, tissue classification, biomarker scoring). Design and implement

scalable, cloud-based workflows

for processing large whole-slide image datasets. Optimize model inference and performance using GPU computing, containerization, and workflow automation tools. Evaluate and validate model performance using appropriate metrics, statistical methods, and explainable AI techniques. Ensure code quality through documentation, version control, and adherence to reproducible research practices. Support integration of developed models and pipelines into existing research and clinical infrastructures.

Requirements

Master's degree in Computer Science, Biomedical Engineering, Electrical Engineering, or a closely related field. Strong background in

deep learning and computer vision

(e.g., CNNs, segmentation networks, transformer-based models). Proven experience in

deploying and optimizing deep learning algorithms

for production or research environments, particularly with

GPU-based or cloud infrastructure

. Proficiency in

Python

and deep learning frameworks such as

PyTorch

or

TensorFlow

. Hands-on experience with

model inference optimization

,

containerization (Docker)

, and

workflow automation

for scalable deployment. Experience with

medical or microscopy image analysis

preferred (e.g., histopathology, radiology). Familiarity with

image processing libraries

(OpenCV, scikit-image),

data handling tools

(NumPy, pandas), and

version control systems

(Git). Experience with

cloud computing platforms

(e.g., AWS, GCP, Azure) and integrating AI models into cloud-based data pipelines. Excellent communication skills and a demonstrated ability to collaborate in an

interdisciplinary research environment

.
Job Type: Full-time

Pay: From $75,000.00 per year

Work Location: Hybrid remote in Toronto, ON M5G 1X7