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