Applied Machine Learning Scientist II (ATH # 5215)

TD•Montreal, QC

5d•CA$125,500 - CA$154,000•Onsite

About The Position

The TD Model Validation (MV) group is responsible for the independent validation and approval of models used for Agentic AI, Generative AI, Natural Language Processing (NLP), credit, fraud, and marketing models. The Artificial Intelligence/Machine Learning (AI/ML) MV team is responsible for the validation of all AI/ML models used across the Bank for various use cases. This position reports to the Senior Applied Machine Learning Scientist (Generative AI) in the AI/ML Model Validation team and is primarily focused on the validation and review of Generative AI and Deep Learning models.

Requirements

Strong quantitative skills with an advanced degree in one or more of the following areas: computer science, machine learning, engineering, statistics, mathematics, or physics.
Proven experience as Generative AI Scientist, Machine Learning Scientist, or a similar role.
Ability to work independently and collaboratively in a fast-paced, dynamic environment. Great time management and multitasking skills with minimal supervision.
Experience with and strong knowledge of AI/ML methodologies including Generative AI, Agentic AI, Deep Learning, modern Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Transformers, Diffusion models.
Experience with Deep Learning and Generative AI technology stacks and libraries such as PyTorch, LangChain, HuggingFace, PromptFlow, etc.
Motivated to stay up to date with the latest advancements in Generative AI, Prompt Engineering, Machine Learning, and Cloud technologies.
Proficient in scripting/programming language - Python.
Familiarity with cloud platforms (e.g., Azure, AWS).
Familiarity with Data Structures, Algorithm design, and principles of Object-Oriented Programming (OOP).
Knowledge of machine learning explainability/interpretability algorithms.
Excellent verbal and written communication skills. The position requires writing clean technical reports.

Nice To Haves

Publications in the relevant conference and journals is a plus.
Ability to implement AI/ML algorithms from academic research papers is a plus.
Knowledge of SQL and database systems.

Responsibilities

Validate (review, test, and provide effective challenge) AI/ML models, particularly Generative AI and Deep Learning models.
Conduct R&D in the area of GenAI / Agentic AI / LLM evaluation, testing, explainability.
Stay up to date with advancements in the field of Generative AI including major publications, important Large Language Models (LLMs), evaluation metrics, technology stacks, and datasets.
Maintain full professional knowledge of techniques and developments in the field of AI/ML and share knowledge with business partners and senior management.
Develop/implement AI/ML model validation methodologies and standards. Ensure that the validation methodologies and standards are in line with industry best practices and address regulatory and audit requirements.
Develop and apply a variety of statistical tests and modeling techniques to identify/recommend improvements to models and undertake related initiatives. Implement benchmark models as applicable.
Communicate findings and recommendations to both technical and non-technical stakeholders.
Work effectively with different internal partners such as AI2, Layer6, P&T, and FCRM.