Machine Learning Engineer

Staff - Non Union

Job Category

M&P - AAPS

Job Profile

AAPS Salaried - Scientific Eng., Level B

Job Title

Machine Learning Engineer

Department

Subramaniam Laboratory | Department of Biochemistry and Molecular Biology | Faculty of Medicine

Compensation Range

$6,747.50 - $9,701.42 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

March 19, 2025

Note: Applications will be accepted until 11:59 PM on the Posting End Date.

Job End Date

Jan 31, 2026

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

The Department of Biochemistry & Molecular Biology at the University of British Columbia invites applications for a Machine Learning Engineer position at 100% FTE to join an interdisciplinary translational research program on pandemic preparedness, PROGENITER, led by Dr. Sriram Subramaniam. PROGENITER’s mission is to bolster Canada’s pandemic preparedness by providing ready-to-deploy protein therapies against viruses with high pandemic potential and ready-to-implement workflows for rapid response to future pandemics. Our research combines novel technologies for AI-enabled and structure guided antibody discovery combined with state-of-the-art capabilities for cryo-EM and biochemistry. More details about our program can be found at http://electron.med.ubc.ca and at http://progeniter.ca.

We are seeking to recruit a highly motivated and exceptional candidates with background in machine learning who will be responsible for developing and applying machine learning approaches to support PROGENITER’s goals for structure-guided biologics design. This role involves developing innovative computational methods and evaluating/applying established methods towards the invention of novel medicines.

Applications should include a letter outlining the applicant’s research, strengths and experiences relevant to the position requirements, a detailed curriculum vitae and the names of three references.

Candidates interested must apply via the UBC Careers website.

Organizational Status

The incumbent will report to the Principal Investigator, Dr. Sriram Subramaniam and work closely with PROGENITER team members, on a daily basis.

PROGENITER is a University of British Columbia-affiliated and nationally recognized multi-year research program to enable the rapid design, engineering, and production of antibody leads against present and future viral diseases to ensure that Canada is better prepared for the next pandemic. PROGENITER is a key component of Canada’s Immuno-Engineering and Biomanufacturing Hub (CIEBH). Our interdisciplinary team of 15 researchers, 5 industry partners, 40-50 staff and students work together to discover, test and advance therapeutics targeting infectious diseases at an accelerated pace.

Work Performed

Reporting to Dr. Sriram Subramaniam, Principal Investigator and Professor in the Department of Biochemistry & Molecular Biology, the successful candidate will be expected to;

• Develop, apply, and iteratively improve AI guided modeling techniques and scientific workflows to accelerate protein design and antibody optimization campaigns via design-make-test-analyze cycles
• Explore use of deep learning models to improve antibody design
• Develop unit testing and regression testing suite for codebase
• Generate quarterly progress reports on project advances and present at weekly team meetings
• Collaborate closely with PROGENITER team members working on experimental aspects of biologics design including cryo-EM structural analysis, biochemistry and neutralization mechanisms
• Stay abreast of the latest advances in the machine learning field and adapt relevant concepts into internal scientific workflows, as needed.
• Provide strategic guidance and advice on general aspects of implementing computational aspects of protein design approaches for PROGENITER
• Serve as the primary contact for programming and code implementation needs in the program
• Perform other duties as required.
  
  

Consequence of Error/Judgement

Failure to maintain a high standard of work with consistent communication between team members and the supervisors will result in delays and quality of research output, which may impact the working conditions, reputation, and funding for the research group and UBC.

Supervision Received

Works independently under general direction from the Principal Investigator, Dr. Sriram Subramaniam. This role will involve working in close collaboration with PROGENITER Team members, including 15 researchers, 5 industry partners, 40-50 staff and students.

Supervision Given

This position may oversee the work of other team member’s work who is involved in developing AI-guided modeling techniques and scientific workflows.

Minimum Qualifications

Undergraduate degree in Engineering or Applied Science. Minimum of three 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

• MSc or PhD degree in computer science, applied math, statistics, bioinformatics, physics, chemistry or a related discipline.
• Two or more years’ experience working with major deep learning frameworks (PyTorch, TensorFlow, JAX etc.) implementing modern deep learning models such as graph neural networks, Transformers, diffusion models.
• Experience working with representation learning and generative AI models.
• In-depth understanding of modern and classical machine learning (ML) methods with practical experience designing, training, and validating such algorithms.
• Experience building scalable, optimized scientific software, and knowledge of GPU computation and CUDA.
• Familiarity with 3D protein structures and protein sequences.
• Demonstrated ability to work at a high level of personal and professional integrity.
• Excellent written and verbal communication skills, including the ability to communicate with scientific and non-scientific personnel.
• Excellent attention to detail with strong critical thinking and decision-making abilities.
• Ability to multitask and thrive in a fast-paced environment with changing priorities.
• Excellent time management skills with the ability to prioritize and meet deadlines.
• Must be an independent, self-starter who is also an excellent team player with strong interpersonal skills.
• Must be adaptable and flexible to work collaboratively and effectively in a multi-disciplinary environment.