Engineer

About The Team

BMO’s Applied AI team is responsible for building high‑performing, safe, and reliable AI systems that power real banking experiences. The Evaluations group within Applied AI develops the methods, datasets, and tooling that measure quality, safety, and performance across the full AI lifecycle. Working closely with product, engineering, and research partners, the team ensures evaluation signals are deeply embedded into training loops, deployment workflows, and continuous monitoring processes. This group operates at the intersection of data science, machine learning, and responsible AI, enabling scalable, repeatable, and trustworthy evaluation of advanced AI systems.

About The Role

The AI Evaluation Scientist is an individual contributor role focused on delivering the data science stream of AI evaluations. This includes designing, implementing, and productionizing evaluation methods, metrics, and datasets that directly influence modeling decisions, product quality, and the safety posture of AI systems across the bank. You will work hands‑on with complex models—particularly LLMs and deep learning systems—developing rigorous empirical analyses that surface model weaknesses, performance trends, and risk signals.

In this role, you will translate evaluation standards into robust, maintainable evaluation code and workflows. You will collaborate with engineers to integrate evaluation signals into CI/CD and training pipelines, and work with product and research partners to ensure evaluation insights meaningfully shape model improvements. This position is highly technical, experimental, and delivery‑oriented, with a strong emphasis on applied data science, reproducible experimentation, and responsible AI practices.

Key Responsibilities

• Design and implement advanced evaluation methods for LLMs and ML systems, including robustness, reliability, fairness, explainability, calibration, and safety‑and-performance-focused metrics.
• Build and maintain high‑quality evaluation datasets, golden sets, challenge sets, and red‑teaming corpora tailored to real banking workflows.
• Develop reusable evaluation harnesses and pipelines that support multi‑agent workflows, tool use, and retrieval‑augmented generation scenarios.
• Conduct empirical analyses, including statistical tests, error analysis, and ablation studies, to identify model weaknesses and guide model and product improvements.
• Integrate evaluation metrics and signals into model training loops, deployment gating checks, and continuous monitoring processes.
• Prototype and validate novel evaluation algorithms inspired by current research in LLM safety, interpretability, and reliability, and convert prototypes into maintainable components.
• Produce clear, actionable evaluation reports that translate technical findings into insights for engineering, modeling, product, and business stakeholders.
• Collaborate with engineering, research, and product teams to align evaluation requirements and deliver production‑ready evaluation capabilities.
• Ensure reproducibility and reliability of evaluation results through dataset versioning, configuration control, testing practices, and documentation.

Qualifications

• 7+ years of experience in data science, machine learning, or AI development, with at least 3 years focused on evaluation, safety, reliability, or model performance analysis.
• Master’s or PhD in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field, or equivalent practical experience.
• Strong proficiency in Python and SQL, with experience using PyTorch or TensorFlow, scikit‑learn, and modern data science libraries.
• Demonstrated experience building evaluation pipelines for LLMs or ML systems, including metric implementation, dataset creation, and CI/CD integration.
• Solid understanding of statistical testing, calibration, sampling design, and error analysis.
• Experience with evaluation of RAG systems, tool‑use workflows, long‑context scenarios, adversarial/jailbreak attacks, toxicity/bias detection, or privacy/PII leakage tests.
• Familiarity with MLOps/LLMOps practices, including experiment tracking, artifact management, and cloud‑based ML infrastructure.
• Strong communication skills with the ability to translate complex evaluation findings for both technical and non‑technical audiences.
• Experience with interpretability or fairness techniques (e.g., SHAP, counterfactuals, model probing) is an asset.
• Contributions to research or open‑source projects in evaluation, safety, reliability, or interpretability are an asset.

Salary:

$122,400.00 - $228,000.00

Pay Type:

Salaried

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit:

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.

As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.

To find out more visit us at

BMO is proud to be an equal employment opportunity employer. We evaluate applicants without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other legally protected characteristics. We also consider applicants with criminal histories, consistent with applicable federal, state and local law.

BMO is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to [email protected] and let us know the nature of your request and your contact information.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.