ML/AI Research Engineer

BMO•Toronto, ON

1d•CA$103,200 - CA$192,000•Onsite

About The Position

Researches, builds, and implements scalable artificial intelligence systems capable of learning and making predictions to business requirements. Enhances data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models. Monitors, evaluates, and optimizes learning processes to continuously improve high-performance models. Works with other data and analytics professionals to optimize, refine, automate and scale analysis into repeatable analytics solutions and decision support tools. Designs and develops machine learning (ML) and deep learning systems. Runs machine learning tests and experiments. Trains and retrains systems to prevent drift and optimize results. Solves complex problems with multi-layered data sets, extends existing ML frameworks and optimizes existing machine learning libraries. Develops Machine Learning apps, implements algorithms, and builds tools to apply ML frameworks. Turns unstructured data into useful information by auto-tagging images and text-to-speech conversions. Develops ML algorithms to analyze huge volumes of historical data to make predictions. Runs tests, performs statistical analysis, and interprets test results. Operates at a group/enterprise-wide level and serves as a specialist resource to senior leaders and stakeholders. Applies expertise and thinks creatively to address unique or ambiguous situations and to find solutions to problems that can be complex and non-routine. Implements changes in response to shifting trends. Broader work or accountabilities may be assigned as needed. Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations.

Requirements

Master's or Ph.D. in Computer Science, Engineering, Mathematics, or a related quantitative field.
5+ years of applied deep learning experience with end-to-end ownership of complex modelling initiatives.
Strong proficiency in Python and PyTorch; familiarity with distributed processing frameworks (e.g., PySpark) is an asset.
Writes clean, modular, well-tested code following software engineering best practices (CI/CD, automated testing, version control, documentation).
Experience working with large structured and unstructured datasets.
Strong experimental rigor with the ability to deliver reproducible research in ambiguous problem spaces.
Effective communicator who can prioritize across multiple initiatives in a fast-paced environment.
Exemplifies high performance, integrity, and partnership.
Intermediate level of proficiency: Systems Thinking.
Advanced level of proficiency: Mathematics, Statistics & Operations Research.
Critical thinking.
Creative reasoning.
Computational Thinking and Programming.
Deep Learning.
Machine Learning.
Scaling Models.
Continuous Integration and Continuous Delivery/Deployment.
ML algorithm.
Verbal & written communication skills.
Analytical and problem solving skills.
Influence skills.
Collaboration & team skills; with a focus on cross-group collaboration.
Able to manage ambiguity.
Data driven decision making.
Typically 7+ years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
Seasoned professional with a combination of education, experience and industry knowledge.

Nice To Haves

Publications at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, TPAMI) are a strong asset.

Responsibilities

Lead the design, training, and scaling of transformer-based foundation models, translating business objectives into production-ready deep learning systems.
Run rigorous experiments against strong baselines to continuously improve model quality, speed, and reliability.
Build reusable tools and pipelines that accelerate research iteration across the team.
Advance the team's research agenda by tracking state-of-the-art research and identifying high-impact opportunities.
Collaborate cross-functionally to evaluate model outputs in downstream applications and measure business impact.
Develop domain expertise to inform modelling decisions and contribute to broader organizational initiatives.
Enhances data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models.
Monitors, evaluates, and optimizes learning processes to continuously improve high-performance models.
Works with other data and analytics professionals to optimize, refine, automate and scale analysis into repeatable analytics solutions and decision support tools.
Designs and develops machine learning (ML) and deep learning systems.
Runs machine learning tests and experiments.
Trains and retrains systems to prevent drift and optimize results.
Solves complex problems with multi-layered data sets, extends existing ML frameworks and optimizes existing machine learning libraries.
Develops Machine Learning apps, implements algorithms, and builds tools to apply ML frameworks.
Turns unstructured data into useful information by auto-tagging images and text-to-speech conversions.
Develops ML algorithms to analyze huge volumes of historical data to make predictions.
Runs tests, performs statistical analysis, and interprets test results.
Operates at a group/enterprise-wide level and serves as a specialist resource to senior leaders and stakeholders.
Applies expertise and thinks creatively to address unique or ambiguous situations and to find solutions to problems that can be complex and non-routine.
Implements changes in response to shifting trends.
Broader work or accountabilities may be assigned as needed.
Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations.