Machine Learning Resident - Client: Medtronic (12 month term)
“Use your machine learning skills to make a tangible impact on healthcare. Join our team of domain experts and machine learning scientists to build, test, and deploy a real-world predictive tool that identifies individuals in need of health interventions.”
- David Staszak, Lead Machine Learning Scientist and Amor Provins, Product Owner, Advanced Technology
About the Role
This is a paid Residency that will be undertaken over a 12 month period. The Resident will report to an Amii Scientist and regularly consult and work with our partners to share insights and engage in knowledge transfer activities. Successful candidates will be members of a cross-functional project team with backgrounds in applied ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly impactful.About the Client
Medtronic is a global healthcare technology leader, working to solve the most challenging health problems facing humanity.About the Project
This project is focused on applying AI and machine learning technology to support pressure injury prevention by helping care teams identify patients at higher risk and intervene earlier. The work will involve developing and evaluating predictive models using health data, with the goal of enabling more proactive, personalized prevention, reducing clinical burden, and improving outcomes for vulnerable patients.Required Skills / Expertise
Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning and keen interest and/or domain experience in health/medicine fields.Key Responsibilities:
- Utilize a range of supervised, unsupervised and potentially NLP methods to build a robust and deployable predictive model.
- Ingest and understand (including performing exploratory data analysis and baseline statistical assessments) medical data from multiple hospital networks and sources
- Experiment and fine-tune the model(s) as necessary based on the performance of baseline and deployment expectations
- Collaborate with the project team and domain experts to iteratively develop models, identify, evaluate and understand clinical features, , and compare to existing clinical evaluation tools
- 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:
- Proficiency and hands on experience in developing and training, optimizing, evaluating, validating and deploying machine learning and deep neural network models in PyTorch and/or TensorFlow
- Proficient in Python programming language and related ML frameworks, libraries and toolkits
- 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:
- Completion of a Computer Science (or a related graduate degree program) MSc. or PhD with specialization in predictive health or medical applications
- 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 using NLP tools and techniques
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)
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 August 4, 2026 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.
