Staff - Non Union Job Category M&P - AAPS Job Profile AAPS Salaried - Information Systems and Technology, Level B Job Title Senior Machine Learning Engineer Department McKeown Laboratory Pacific Parkinson's Research Centre Division of Neurology | Department of Medicine | Faculty of Medicine Compensation Range $6,251.00 - $8,986.00 CAD Monthly The Compensation Range is the span between the minimum and maximum base salary for a position. The midpoint of the range is approximately halfway between the minimum and the maximum and represents an employee that possesses full job knowledge, qualifications and experience for the position. In the normal course, employees will be hired, transferred or promoted between the minimum and midpoint of the salary range for a job. Posting End Date December 3, 2024 Note: Applications will be accepted until 11:59 PM on the Posting End Date. Job End Date Dec 1, 2025 At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the necessary conditions for a rewarding career. Job Summary We are looking for a highly experienced Senior Machine Learning Engineer to lead the design and development of advanced AI systems, with a strong focus on deep learning applications in healthcare. This position requires a strong background in machine learning, embedded-AI, and production software systems, as well as the ability to collaborate closely with medical experts. The overall project is to develop a software platform enabling app development for automated and remote disease assessment Organizational Status Work closely with multidisciplinary teams, including researchers, clinicians, and software developers, to ensure seamless integration and functionality of assessment algorithms within the application. Work Performed Machine Learning Solution Development: Investigate and harness cutting-edge machine learning methods, including deep learning, computer vision and large language models (LLMs), to address diverse challenges in the medical field. Build and deploy state-of-the-art deep learning models specifically tailored for healthcare applications. Develop and deploy robust, low-maintenance applied AI solutions in biomedical fields. Full Lifecycle System Design: Manage the complete machine learning system lifecycle, from conceptualization and R&D to deployment and optimization, ensuring that all solutions are scalable, secure, and high-performing. Team Leadership: Lead junior engineers, guiding them on the development of both AI models and software solutions. Foster a culture of technical excellence and collaboration. Advanced Software Engineering Practices: Implement advanced software engineering principles such as CI/CD, modular design, and containerization to build robust, maintainable, and scalable machine learning systems. Research and Innovation: Stay at the forefront of AI and healthcare trends, constantly integrating new methods and research findings into the development of specific healthcare applications. Consequence of Error/Judgement Errors in this role can have significant effects, impacting both the accuracy of research findings and the subsequent care of patients. Flaws in video analysis, algorithm design, or data interpretation could lead to misleading research outcomes, which might compromise the credibility of the study and delay essential advancements in patient care. The Machine Learning Engineer must therefore exhibit careful attention to detail, uphold stringent lab protocols, and ensure compliance with ethical standards when handling patient data. Supervision Received The Machine Learning Engineer will be under the direct supervision of the Principal Investigator. This position will benefit from structured instructions from senior laboratory members who will provide ongoing guidance on project execution and strategic direction. Regularly scheduled meetings will ensure that the technician remains aligned with project goals, can address emerging challenges promptly, and receives constructive feedback to foster professional growth. Supervision Given The Machine Learning Engineer will provide guidance and technical supervision to a diverse group of team members, including undergraduate students, graduate students and junior engineers. This role is responsible for advising on the application of machine learning techniques in healthcare research, ensuring that methodologies are appropriate and effectively implemented. The engineer will also facilitate knowledge transfer and skill development among team members to enhance their research and development capabilities. Minimum Qualifications Undergraduate degree in a relevant discipline. Minimum of two years of related experience, or the equivalent combination of education and experience. - Willingness to respect diverse perspectives, including perspectives in conflict with one's own - Demonstrates a commitment to enhancing one's own awareness, knowledge, and skills related to equity, diversity, and inclusion Preferred Qualifications • Significant experience in Software Development and Machine Learning Engineering, with strong expertise in deep learning, particularly in building and deploying machine learning systems at scale. • Master's Degree in Computer Science, Machine Learning, Data Science, Bioinformatics, or a related field. • Software System Development: Proven experience in designing and developing production-level machine learning systems and software architectures. • Deep Learning Expertise: Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras, and experience developing models like CNNs, RNNs, transformers, and other advanced architectures. • Programming Languages: Mastery of Python for AI/ML development, with additional experience in production-level languages such as C++, Java, or Scala. • Advanced Software Engineering Skills: Strong proficiency in CI/CD, modular software development, containerization (Docker, Kubernetes), and cloud services (AWS, GCP, Azure). • Team Leadership and Communication: Proven ability to lead a technical team, manage complex projects, and effectively collaborate across multidisciplinary teams, including medical professionals. • Ph.D. in Computer Science, Software Engineering, Machine Learning, or related fields. • Experience in healthcare AI applications, especially related to neurodegenerative diseases. • Strong knowledge of biomedical signal processing and deep learning. • Experience on IOT devices and medical applications • Experience with federated learning, privacy-preserving AI, or AI ethics in healthcare. • MLOps Expertise: Experience deploying machine learning models into production environments, with a strong focus on scalability, monitoring, and continuous improvement through MLOps practices. 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