Cognitive Engineer Lead

Role Overview: You will be a Cognitive Engineer in the Transformative AI team within the Asset and Wealth Management, focusing on analyzing, modeling, and designing multimodal humanAI systems aligned with human cognition. Your role will involve optimizing decision-making, information flows, and human-agent interactions across voice, text, data visualization, and ambient interfaces. Unlike traditional UI/UX design, your main focus will be on understanding cognition and human performance in complex environments to engineer systems that extend and amplify these capabilities. Key Responsibilities: - Conduct cognitive task analyses for multimodal workflows including voice, chat, visual dashboards, and ambient signals. - Translate insights into system-level requirements for AI agents, decision support tools, and automation pipelines. - Model human workload, attention, and modality-switching costs to enhance user experience between text, charts, and speech. - Collaborate with product, design, and engineering teams to ensure multimodal systems adhere to cognitive principles rather than just interface aesthetics. - Design and evaluate cross-modal decision support, determining when AI should speak, show information, or remain silent. - Develop frameworks for trust calibration and cognitive fit in multimodal humanAI teaming. - Conduct simulations and user-in-the-loop experiments to assess system performance across different modalities. Qualifications Required: - Formal training or certification in software engineering concepts with a minimum of 5 years of applied experience. - Advanced degree in Cognitive Engineering, Human Factors, Applied Cognitive Psychology, Systems Engineering, or a related field. - Proven experience in complex, high-stakes domains where engagement is intricate. - Deep expertise in cognitive load and modality management, human error analysis and mitigation, decision-making under uncertainty, human-automation interaction, and voice/visual trust calibration. - Experience in evaluating multimodal AI/ML systems including voice, NLP, data visualization, and multimodal agents. (Note: Preferred qualifications, capabilities, and skills were not included in the Job Description provided.),