ML Research Engineer ​/ Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a ML Research Engineer / Scientist based in Canada.
Join a research-driven team building next-generation AI models designed to understand complete CT studies rather than isolated findings.
You’ll work on foundation models, vision-language learning, and multi-finding detection using medical imaging data at unprecedented scale.
Your research will have a direct path from experimentation to regulatory submissions, hospital deployment, and real-world patient care.
You’ll own models and experiments end to end, from developing the initial idea through training, evaluation, calibration, and production readiness.
You’ll work alongside experienced ML engineers, software engineers, and fellowship-trained radiologists across multiple clinical specialties.
The role combines deep technical research with practical impact, giving you the opportunity to solve challenging problems in medical AI with exceptionally rich real-world data.
This is a fully remote opportunity for an independent researcher who wants their work to move quickly from the lab into clinical practice.
Accountabilities   Design, develop, train, and evaluate machine learning models capable of interpreting complete CT studies at the study level.
Research foundation-model approaches for medical imaging, including 3D and volumetric learning at large scale.
Develop and investigate vision-language models that connect medical images with the terminology and reporting patterns used by radiologists.
Build models capable of identifying and prioritizing multiple urgent clinical findings simultaneously while maintaining safe and clinically appropriate operating points.
Design and execute independent experiments, from hypothesis formation and architecture selection through training, evaluation, and analysis.
Develop custom architectures, training pipelines, loss functions, and distributed training approaches using modern deep learning frameworks.
Analyze model performance rigorously and establish reproducible evaluation methodologies suitable for clinically consequential AI systems.
Work closely with fellowship-trained radiologists to understand clinical requirements, interpret results, and translate research findings into practical model improvements.
Contribute to models and research that progress toward regulatory submissions, clinical deployment, and real-world patient use.
Take ownership of research projects end to end and make informed decisions about which experiments and approaches are most likely to deliver meaningful improvements.
Collaborate with ML and software engineering teams to move successful research from experimentation toward robust, deployable systems.
Requirements:
Strong practical experience with modern machine learning and deep learning, particularly using  PyTorch  for custom architectures, training loops, and experimentation.
Deep understanding of why machine learning architectures, objectives, optimization strategies, and training approaches work, rather than relying solely on existing implementations.
Demonstrated ability to independently formulate hypotheses, design experiments, interpret results, and iterate toward better models.
Strong understanding of rigorous experimentation, evaluation, reproducibility, and model validation.
Experience working with large-scale datasets and distributed training environments is highly valuable.
A strong interest in solving technically challenging problems where model performance and reliability have meaningful real-world consequences.
Ability to work effectively with researchers, engineers, and clinical experts in a collaborative environment.
Medical imaging, 3D computer vision, or volumetric-data experience is advantageous but not required.

Experience with vision-language models or self-supervised learning is a plus.
Familiarity with DICOM, CT imaging, radiology, or other medical-data formats and workflows is beneficial.
A PhD, research publications, or a strong academic research background is a plus, but not a prerequisite.
Prior medical-AI experience is  not required ; a willingness to…