Principal AI/ML Scientist

Vanguard•Toronto, ON

6d•Hybrid

About The Position

Vanguard’s Enterprise AI and Research (EAIR) team is actively working on advancing AI innovation by integrating cutting-edge concepts into the Vanguard AI ecosystem, establishing strategic AI partnerships, and building enterprise-level AI capabilities to empower Vanguard clients with advanced AI research and technology. The team aims to ultimately accelerate critical business solutions with AI capabilities. Some of the team’s areas of research include areas around Agentic AI, Responsible AI, and Cognitive AI Architecture. As a Principal AI Research Scientist in the EAIR team, you will drive groundbreaking research in Large Language Models, Agentic AI, Responsible AI, and Cognitive AI Architecture, while contributing significantly to the broader AI research community at Vanguard. You will bring your extensive experience in AI principles, methodologies, and tools to lead cutting-edge projects, author high-impact publications, and represent the organization at conferences and workshops. In addition to your expertise in AI, you will foster strong relationships with university/industry partners, identify strategic alignment opportunities, and facilitate productive collaborations to advance both fundamental and applied AI research. Your ability to combine deep technical knowledge with strong communication skills will enable you to effectively translate complex concepts for diverse audiences, ensuring the organization remains at the forefront of AI innovation and ethical practices.

Requirements

PhD or Master in a relevant discipline such as Computer Science, Cognitive Science, Mathematics, Statistics, Physics, Electrical & Computer Engineering.
At least 10+ years of experience in AI research in industry or academic setting
Strong expertise in various AI/ML concepts and paradigm.
Strong expertise in at least one or more of the following areas: Large Language Models, Natural Language Processing, Reinforcement Learning, Knowledge Graph, Time-Series Analysis, or Generative AI.
Experience with machine learning development lifecycle and AI/ML methods such as Transformers, Diffusion Models, SHAP, LLM and GenAI etc.
Strong software engineering capabilities and hands-on experience with various machine learning and deep learning frameworks including numpy, scikit-learn, keras, PyTorch and Tensorflow
A strong understanding of the real-world advantages and drawbacks of various algorithms and the ability to measure success.
Ability to write clean, understandable code that follows leading industry standards and practices and is well-documented, and to build easily reproducible models.

Responsibilities

Research on new and innovative techniques to solve Artificial Intelligence/Machine Learning problems, supporting Vanguard strategic business goals. Within, but not limited to the domain of LLM, Autonomous Agents, Knowledge Graph, AI in Finance, Generative AI and Reinforcement Learning.
Serves as an expert on cross-functional teams for large strategic initiatives.
Educate and train business stakeholders and leaders to adopt AI research innovation.
Lead AI research and strategies that would create long-term business benefits to Vanguard.
Facilitate research collaboration with University Partners.
Publish findings to AI/ML conferences including NeurIPS, ICML, ICLR, EMNLP, ACL, AAAI etc.
Leads and executes deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making.
Creates alternative model approaches to assess the design of complex models and advance future capabilities.
Lead R&D of industry-leading algorithms for LLM finetuning paradigms, Autonomous Agents, Knowledge Graph, Generative AI, Responsible AI and their applications in financial domains to significantly improve our Vanguard client experiences.
Guide and mentor other research scientists and engineers in the development and implementation of technical roadmaps.
Engage with internal stakeholders to understand and explore business processes to develop hypotheses.
Brings structure to requests and translates requirements into an analytical approach.
Collaborate with various business and operation units to identify business opportunities and design innovative solutions to optimize processes and promote informed decision-making.
Work closely with product teams and mentor them on modern ML best practices, and keep the wider team informed of the state of the art.