Vanguard is one of the world’s largest and most respected investment management companies, known for its client-centric approach, low-cost investment products, and commitment to long-term, data-driven investment strategies. Our 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.

Qualification:

• Master’s degree in a relevant field of study such as Computer Science, Cognitive Science, Mathematics, Statistics, Physics, Electrical & Computer Engineering.
• At least 5+ years of experience in AI/ML research in industry or academic setting.
• Experience with machine learning development lifecycle and AI/ML methods such as Transformers, Diffusion Models, SHAP, LLM and GenAI etc.
• Deep understanding of cutting-edge AI research topics, such as multi-agent systems, generative models, or cognitive architectures
• 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 model

ROLE OVERVIEW:

We are seeking a highly skilled and motivated Senior AI Research Scientist to join our AI research team at Vanguard. In this role, you will be responsible for conducting innovative research in the areas of Large language models, Agentic AI, Reinforcement Learning, Knowledge Graph, and Responsible AI learning paradigms. You will work closely with our interdisciplinary team of scientist, engineers, and product managers to advance the state-of-the-art in AI technologies and drive their practical application in real-world Vanguard scenarios.

CORE RESPONSIBILITIES:

• Research on new and innovative techniques to advance Artificial Intelligence research supporting Vanguard strategic business goals.
• Contribute to advanced AI research that would bring transformative changes to Vanguard business. Publish findings to AI/ML conferences including NeurIPS, ICML, ICLR, EMNLP, ACL, AAAI etc.
• Build 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.
• Performs deep-depth diagnostic, predictive and prescriptive analysis to support data-based business decision-making. Creates alternative model approaches to assess the design of complex models and advance future capabilities.
• Design, implement, test, deploy, and maintain innovative data and machine learning solutions to accelerate our business.
• Create business values through productization and deployment of research outcomes.
• Engages 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.

Qualification:

• Master’s degree in a relevant field of study such as Computer Science, Cognitive Science, Mathematics, Statistics, Physics, Electrical & Computer Engineering.
• At least 5+ years of experience in AI/ML research in industry or academic setting.
• Experience with machine learning development lifecycle and AI/ML methods such as Transformers, Diffusion Models, SHAP, LLM and GenAI etc.
• Deep understanding of cutting-edge AI research topics, such as multi-agent systems, generative models, or cognitive architectures
• 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