Machine Learning Engineer

Closing date
November 30, 2021

Machine Learning Engineer

The primary function of the Machine Learning Engineer is to develop and optimize next-generation intelligent translation using Machine Learning (ML) systems and natural language processing for the information collected in RNAO data systems: Nursing Quality Indicators for Research and Evaluation (NQuIRE®) and MyBPSO. The role of Machine Learning Engineer is to develop and test algorithms and deploy solutions to key questions in guideline implementation and monitoring activities that are informed by high-quality evaluation data, thereby enhancing evidence-based nursing practice , education and decision-making – locally, provincially and internationally. This role is under a collective agreement with a starting salary of $69,141 and on the 4th anniversary to $87,799.


  • develop machine learning algorithms and integrate the ML models into the data system.
  • manage large datasets, perform data extractions, analyze data sets, and develop algorithms based to build/train/evaluate ML models.
  • evaluate ML models and compare with different data system, in-house and externally
  • collaborate with the Evaluation and Monitoring team as well as IMT team to improve the internal process and integrate the ML models into the data system. 
  • support and assist in designing and building tools that support ML projects.
  • perform regular maintenance and updates of the ML system and tools in order to better inform accuracy and predictions related to ML models
  • routinely assess, monitor and track the accuracy, quality and integrity of the data system through audit and observation of data collection, entry and processing methods.
  • ensure and validate data quality of the information collected from end users.
  • work with team members to prepare, analyze and interpret reports. 
  • assist with drafting and preparing abstracts and manuscripts for presentation and publication, or as otherwise intended by the NQuIRE® team.
  • work collaboratively with senior management, the data quality team, project coordinators and IT Data Base/Web Developers using strategic and tactical approaches to develop, implement, and maintain ML tools, data base functionality and enhancements.
  • be a champion for Machine Learning technologies and conduct research on data problems, literature review and develop appropriate methodology.
  • produce work updates, status reports and presentations for staff and stakeholders as required.


  • Master's degree in Computer Science, Computer Engineering, or Machine Learning.
  • pp to five years’ experience including at least one year of experience in algorithm development, data science, data engineering, and knowledge in health outcomes.
  • solid understanding of foundational health informatics, statistical methods and Machine learning algorithms. This may include but is not limited to knowledge of regression analysis, fuzzy logic, decision tree and Naïve Bayes etc.
  • deep understanding of Natural Language Processing and Natural Language Understanding
  • ability to identify methodological problems associated with survey data systems and database development or data analysis.
  • hands-on-experience of working with deep learning libraries in Python or R
  • excellent technical skills including but not limited to the knowledge of information audit processes, Clinical Information / Management Systems, MS-Office products.
  • flexible and adaptive in responding to new projects and priorities as they arise.
  • excellent analytical skills and detail-oriented.
  • skill in scientific writing and oral communication and interpersonal skills to deal effectively with staff and stakeholders, synthesize and present information, and provide program support.
  • dexterity required to operate a computer, telephone, and other office equipment.
  • ability to communicate effectively orally and in writing in English (French, Spanish, Portuguese or Mandarin are assets but not required).


  • may be required to travel to official sites for observation and assessment of data collection, including possibility of overnight stays (travel reimbursement will be provided)
  • workflow requires continuous re-prioritizing of workload, meeting multiple demands, and working overtime as required.
  • continuous concentration is required to read, screen, and appraise a large volume of information daily, prepare data tables and summary reports, and to consult as appropriate.
  • sight, touch and hearing are used to read, proofread, write, and analyze information and to answer enquiries.
  • sitting is required frequently in the job for more than 2 hours at a time to read and write information, work on the computer, answer the telephone, and attend meetings.


  • works in a climate-controlled office with some exposure to distractions, interruptions and stress from multiple demands.
  • typical conditions are such that no risk or injury is present.