University Health Network

Machine Learning Specialist

University Health Network$98K — $148K *
Healthcare
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Machine Learning, Statistics, Applied Mathematics, Computer Science, or related fields required.
  • 3-5 years of practical or related experience required.
  • Preferred: Graduate degree (MSc or PhD) in a quantitative discipline with experience in clinical or biomedical datasets.
  • Proficient in machine learning frameworks and NLP/LLM methods for structured and unstructured clinical data.
  • Ability to work in a multidisciplinary team and communicate findings to technical and clinical audiences.

Responsibilities

  • Develop and apply machine learning and LLM methods to oncology data sources for cancer risk prediction.
  • Design and evaluate predictive models for cancer-related outcomes in collaboration with clinical teams.
  • Contribute to the full research lifecycle, including data preparation and model validation and dissemination.

Benefits

  • Competitive offer packages.
  • Pension plan membership with the Healthcare of Ontario Pension Plan (HOOPP).
  • Close access to transit and UHN shuttle service.
  • Flexible work environment.
  • Opportunities for career development and promotions.
  • Corporate discounts on travel, restaurants, parking, phone plans, and auto insurance, along with on-site gyms.
Full Job Description
Job Description

Union: Non-Union
Number of Vacancies: 1
New or Replacement Position: New
Site: Princess Margaret Cancer Centre
Department: DMOH
Reports to: Senior Clinical Research Program Manager
Salary Range: $98,738 - $148,107 Per Year
Hours: 37.5 Hours Per Week
Shifts: Monday - Friday; Days
Status: Permanent Full-time
Closing Date: June 9, 2026

Position Summary:
We are seeking a Machine Learning Specialist to join our research team. This individual will start with leadership roles for two ongoing high-impact projects: 1) a trial of AI-generated warnings for treatment-related side effects and 2) a study using routinely collected electronic health record data for cancer screening and early detection. This will be collaborative research within our active research group of engineers, students, and healthcare professionals, who have a broad range of projects ongoing.

Duties:
  • Develop and apply machine learning and large language model (LLM) methods to diverse oncology data sources, including electronic health records, free-text clinical notes, and multimodal biomedical data, to support cancer risk prediction, symptom monitoring, and clinical decision-making
  • Design and evaluate predictive models for cancer-related outcomes such as treatment adverse events, thrombosis risk, palliative care needs, and surgical prognosis, in collaboration with clinical and research teams
  • Contribute to the full research lifecycle including data preparation, model development, validation across internal/external datasets, and dissemination through manuscripts and scientific presentations


Qualifications
  • At minimum, Bachelor's Degree in Machine Learning, Statistics, Applied Mathematics, Computer Science, or related quantitative disciplines, required
  • At minimum, 3-5 years practical experience or related experience, required
  • Graduate degree (MSc or PhD) in a quantitative field such as computer science, information science, biostatistics, or a related discipline, with hands-on experience applying machine learning to real-world clinical or biomedical datasets, preferred
  • Proficiency in machine learning frameworks and NLP/LLM methods, with demonstrated ability to work with structured clinical data (e.g., EMR, lab values), unstructured text (e.g., clinical notes, pathology reports), and/or imaging data
  • Strong capacity to work in a multidisciplinary research environment, communicate findings to both technical and clinical audiences, and contribute to peer-reviewed publications


Additional Information

In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.
  • Competitive offer packages
  • Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP https://hoopp.com/)
  • Close access to Transit and UHN shuttle service
  • A flexible work environment
  • Opportunities for development and promotions within a large organization
  • Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)

Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.

All applications must be submitted before the posting close date.

UHN uses email to communicate with selected candidates. Please ensure you check your email regularly.

Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.

About University Health Network

University Health Network (UHN) is a healthcare organization that provides patient care, research, and education services. The organization operates several hospitals and clinics in Toronto, Ontario, including Toronto General Hospital, Toronto Western Hospital, and Princess Margaret Cancer Centre. UHN offers a range of medical services, including cancer care, cardiovascular care, neurosciences, transplantation, and rehabilitation. The organization is affiliated with the University of Toronto and is one of Canada's largest research hospitals. UHN employs over 16,000 people and serves patients from across Canada and around the world.
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