Artificial Intelligence Research Lead

Job responsibilities

• Work on multiple commercially oriented research projects in collaboration with internal data scientists, applied engineering teams and stakeholders across businesses e.g. Commercial & Investment Banking (including Markets), Asset & Wealth Management, Consumer & Community Banking, etc.
• Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments and rigorous evaluations, synthesize and communicate results, and deliver well-tested, high-quality code.
• Contribute to high-impact business applications, reusable assets and products, and research initiatives.
• Provide thought leadership on internal and external forums through white papers, publications and presentations.
• Lead projects or major workstreams in larger projects/initiatives. Play a key role in ensuring that problem definitions and solutions are technically sound, generalizable and capture business/product requirements.
• Proactively identify and propose new research initiatives motivated by long-term business needs.

Required qualifications, capabilities, and skills

• PhD in Computer Science, Engineering, or related fields with relevant research & work experience in AI/ML
• Research publications in top-tier AI/ML venues (e.g., conferences, journals) – broad conferences such as NeurIPS, ICML, ICLR, etc., or highly regarded specialized conferences such as ICAPS, CRYPTO, etc.
• Deep understanding of fundamental AI/ML techniques and a strong grasp of current state of the art in specific areas of expertise.
• Effective verbal and written communication skills with the ability to address both technical and business audiences.
• Practical software development experience in collaborative project settings such as open-source projects or industry experience. Ability to deliver modular, optimized, high-quality Python code with tests.
• Experience leading and mentoring AI researchers, partnering with engineering and business stakeholders to drive execution and strategic planning across near-term deliveries and longer-horizon research initiatives.
• Demonstrated ability to translate research into real-world impact e.g., production-ready solutions, prototypes/pilots, reusable components, reference architectures, or standards.
• Strong track record of research in one of the following areas: (a) Multimodal model/agent security and safety, alignment, guardrails, red teaming, adversarial attacks and defenses, robustness to distribution shifts (b) Foundation model training for structured data modalities (e.g. tabular, graph) (c) Reinforcement Learning, continual learning (parametric/model-based), model post-training and test-time adaptation.

Preferred qualifications, capabilities, and skills

• Practical experience with ML libraries (e.g. PyTorch, TensorFlow/Keras, HuggingFace Transformers etc.), agent frameworks (e.g. LangGraph, Google ADK, etc.), common formal verification tools and languages (e.g., Coq/Isabelle/Lean, TLA+, Alloy, Z3, PDDL).

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