Machine Learning Resident - Client : Zamplo (1 Year)

Alberta Machine Intelligence Institute; Edmonton; On-site; CAD 50,000 - 70,000; Full time

Job summary

A leading AI research institute is seeking a Machine Learning Resident to improve patient outcomes through predictive modeling. This paid twelve-month residency involves collaboration with healthcare providers and model development. Ideal candidates will have a Master's or PhD in Computer Science, strong machine learning skills, and a passion for healthcare innovation. Opportunities for professional development and networking are included.

Benefits

Mentorship from AI experts

Professional development activities

Network access within the AI community

Qualifications

• Experience with health care data and predictive modeling.
• Familiarity with Linux and Git.
• Strong statistical background for model validation.

Responsibilities

• Build and deploy predictive models using supervised and unsupervised methods.
• Collaborate with the project team and domain experts.
• Prepare presentations and reports for stakeholders.

Skills

Machine learning

Data analysis

Python programming

Education

Master's or PhD in Computer Science or related field

Tools

PyTorch

TensorFlow

Job description

Overview

“Join us for a unique ML Resident role focusing on improving patient outcomes by predicting risk of an adverse event leading to an emergency room visit using ML/DL. You'll work in a fast-paced and dynamic team of machine learning scientists, healthcare providers, and domain experts.”

- Soumik Farhan, Machine Learning Scientist

Description

About the Role

This is a paid residency undertaken over a twelve-month period with the potential to be hired by our client afterwards. The resident will report to an Amii Machine Learning Scientist and regularly consult with the Client team to share insights and engage in knowledge transfer activities.

About our Client

Zamplo is a digital health company transforming the way individuals, clinicians, and researchers engage with health data. Its connected health platform empowers people to track and share their own health information, while providing real-time insights that support better care and outcomes. Zamplo develops innovative, patient-centered tools that reduce costs and improve collaboration across the healthcare system.

The Zamplo platform offers two complementary products:

• Zamplo App: Allows patients to track symptoms, medications, routines, and share information with their care team. It helps patients better manage their health and prepare for meaningful conversations with providers.
• Zamplo Research: An electronic data capture platform designed for researchers, clinicians, and organizations. With patient consent it enables collection of patient-reported outcomes and wearable data, supports electronic consent, and streamlines data management to advance clinical research and improve care delivery.

About the Project

The project aims to use machine learning to classify a patient’s risk of an adverse event leading to an emergency room visit following radiation therapy. This risk assessment will use socio-demographic, non-medical and medical data. By identifying patients with a higher risk of an adverse event earlier, patients, healthcare providers, caregivers, and community stakeholders can implement proactive measures to improve patient outcomes.

As research in this specific area is limited, the resident would approach the classification problem in a number of ways, each with its own set of experiments and models to determine the best option to implement. Following model development, the results will be externally validated in both retrospective and prospective manners.

Required Skills / Expertise

We’re looking for a talented and enthusiastic individual with solid knowledge of machine learning experience working with health care data, and a passion to improve individual health outcomes.

Key Responsibilities

• Utilize a range of supervised, unsupervised methods to build a robust and deployable predictive model.
• Ingest and understand medical data from multiple patient health data sources, including performing exploratory data analysis and baseline statistical assessments.
• Experiment and fine-tune the model(s) as necessary based on baseline performance and deployment expectations.
• Collaborate with the project team and domain experts to iteratively develop models and define identified health event outcomes.
• Implement model explainability and interpretability techniques to provide insights to patients and clinicians.
• Participate in regular meetings with the client and stakeholders, preparing presentations and reports.
• Research and implement different approaches to the problem from literature as well as publicly available tools.

Required Qualifications

• Completion of a Computer Science (or a related graduate degree program) MSc. or PhD with specialization in predictive health or medical applications.
• Proficient in developing and training, fine-tuning and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow.
• Proficient in Python programming language and related ML frameworks, libraries and toolkits.
• Solid understanding of classical statistics and its application in model validation.
• Familiarity with Linux, Git version control, and writing clean code.
• A positive attitude towards learning and understanding a new applied domain.
• Must be legally eligible to work in Canada.

Preferred Qualifications

• Familiarity with and hands-on experience with medical and/or health data.
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
• Experience/familiarity with software engineering best practices.
• Experience using cloud platforms (GCP, AWS, Azure, etc.)

Non-Technical Requirements

• Desire to take ownership of a problem and demonstrated leadership skills.
• Interdisciplinary team player enthusiastic about working together to achieve excellence.
• Capable of critical and independent thought.
• Able to communicate technical concepts clearly and advise on the application of machine intelligence.
• Intellectual curiosity and the desire to learn new things, techniques, and technologies.

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Work under the mentorship of an Amii Scientist for the duration of the project.
• Participate in professional development activities.
• Gain access to the Amii community and events.
• Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer).
• Build your professional network.
• The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion).

Location: Alberta Preferred

About Amii

One of Canada’s three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world’s top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

How to Apply

If this sounds like the opportunity you've been waiting for, please don’t wait for the closing September 22, 2025 to apply - we’re excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won’t be used in the selection process.