Machine Learning Specialist

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: $98738 - $148107 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 Bachelors 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 :

Why join UHN

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 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 UHNs 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.

UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.

We thank all applicants for their interest however only those selected for further consideration will be contacted.

Remote Work :

No

Employment Type :

Full-time