ML Research Engineer / Scientist

Jobgether; Canada; Remote; CAD 120,000 - 190,000; Full time

Benefits offered by this job

Fully remote position

Global location flexibility

Transparent local-market pay

Job summary

Jobgether in Canada is seeking an ML Research Engineer/Scientist to advance medical imaging AI. You will work on foundation models, 3D learning, and vision-language systems with real-world clinical impact.

The role blends rigorous research with practical deployment, offering remote work, and collaboration with radiologists and engineers on FDA-ready workflows.

Qualifications

• Strong practical experience with modern ML and deep learning, particularly using PyTorch for custom architectures and experimentation.
• Ability to independently formulate hypotheses, design experiments, interpret results, and iterate toward better models.
• Rigorous experimentation, evaluation, reproducibility, and model validation.

Responsibilities

• Design, train, and evaluate ML models interpreting complete CT studies at the study level.
• Research foundation-model approaches for medical imaging, including 3D learning at large scale.
• Develop and investigate vision-language models connecting medical images with radiology terminology.
• Collaborate with radiologists and engineers to translate research into practical model improvements.
• Lead experiments end-to-end from hypothesis to deployment-ready production.

Skills

PyTorch

Deep learning

Experimentation

Large-scale datasets

Distributed training

Education

PhD

Tools

Vision-language models

DICOM/CT imaging workflows

Job description

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 learn clinical concepts directly from radiology experts is valued.
• Strong written and verbal communication skills and the ability to work independently in a fully remote environment.

Benefits:

• Fully remote position open to candidates worldwide.
• Location-flexible compensation with a cash-weighted base salary determined according to the local market in the country where you work.
• Specific compensation range for your location shared early in the hiring process.
• No equity included in international offers, with compensation structured transparently around local-market cash pay.
• Opportunity to work with a real-world CT dataset covering approximately 10 million patients, paired with radiology reports.
• Direct collaboration with fellowship-trained radiologists across areas including chest, body, MSK, neuro, and oncology.
• Opportunity to work on research that can progress from experimentation to FDA submissions, hospital deployments, and patient care within months.
• Exposure to large-scale foundation models, vision-language learning, distributed training, medical imaging, and clinically focused AI evaluation.
• High degree of ownership over research ideas, experiments, models, and technical direction.
• Opportunity to work alongside researchers and engineers with significant contributions to medical AI, open datasets, algorithms, and clinical AI systems.
• A small, research-oriented team where successful ideas can move quickly from research into production.