Cognitive Engineer Lead

Are you passionate about the intersection of human cognition and artificial intelligence Join our Transformative AI team and help shape the future of multimodal human-AI systems.

As a Cognitive Engineer in the Transformative AI team within the Asset and Wealth Management, you will analyze, model, and design multimodal human-AI systems that align with human cognition. You will ensure that decision-making, information flows, and human-agent 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.

Job Responsibilities

• 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 human-AI teaming.
• Runs simulations and user-in-the-loop experiments to test system performance across modalities.

Required qualifications, capabilities, and skills

• 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, human-automation interaction and voice/visual trust calibration.
• Experience evaluating multimodal AI/ML systems (voice, NLP, data viz, multimodal agents).

Preferred qualifications, capabilities, and skills

• Analyze how humans think and decide across voice, text, and visual modalities.
• Translate cognitive principles into engineering requirements for multimodal AI systems.
• Ensure our systems work with an understanding of human cognition across all interaction modes
• Has experience in designing and testing multi-modal systems