Cognitive Engineer Lead

As a Cognitive Engineer in the Transformative AI team within the Asset and Wealth Management, you will analyze, model, and design multimodal humanAI systems that align with human cognition. You will ensure that decision-making, information flows, and humanagent interactions are optimized across voice, text, data visualization, and ambient interfaces. Unlike traditional UI/UX design, this role focuses on understanding cognition and human performance in complex environments, then engineering systems that extend and amplify those capabilities. - Conducts cognitive task analyses for multimodal workflows (voice, chat, visual dashboards, ambient signals). - Translates insights into system-level requirements for AI agents, decision support tools, and automation pipelines. - Models human workload, attention, and modality-switching costs (e.g., moving between text, charts, and speech). - Collaborates with product, design, and engineering teams to ensure multimodal systems reflect cognitive principles, not just interface aesthetics. - Designs and evaluates cross-modal decision support e.g., when should an AI speak, when should it show, and when should it stay silent. - Develops frameworks for trust calibration and cognitive fit in multimodal humanAI teaming. - Runs simulations and user-in-the-loop experiments to test system performance across modalities. - Formal training or certification on software engineering concepts and 5+ years of applied experience. - Advanced degree in Cognitive Engineering, Human Factors, Applied Cognitive Psychology, Systems Engineering, or related field. - Proven experience in complex, high-stakes domains where engagement is complex - Deep expertise in: cognitive load and modality management, human error analysis and mitigation, decision-making under uncertainty, humanautomation interaction and voice/visual trust calibration. - Experience evaluating multimodal AI/ML systems (voice, NLP, data viz, multimodal agents). Analyzing how humans think and decide across voice, text, and visual modalities, translating cognitive principles into engineering requirements for multimodal AI systems, and ensuring that systems work with an understanding of human cognition across all interaction modes are some preferred qualifications, capabilities, and skills for this role.,