Vice President-AI 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. In this role, you'll engineer solutions that make decision-making, information flows, and human-agent interactions more efficient, safe, and intuitive. Be part of a team that is redefining how people and technology work together.

As an AI Cognitive Engineer in the Transformative AI team, 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.

• Conduct cognitive task analyses for multimodal workflows (voice, chat, visual dashboards, 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 (e.g., moving between text, charts, and speech)
• Collaborate with product, design, and engineering teams to ensure multimodal systems reflect cognitive principles
• Design and evaluate cross-modal decision support (e.g., when should an AI "speak," "show," or "stay silent")
• Develop frameworks for trust calibration and cognitive fit in multimodal human-AI teaming
• Run simulations and user-in-the-loop experiments to test system performance across modalities

• Formal training or certification in software engineering concepts and at least 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
• 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 visualization, multimodal agents)

• Ability to analyze how humans think and decide across voice, text, and visual modalities
• Skill in translating cognitive principles into engineering requirements for multimodal AI systems
• Experience ensuring systems work with an understanding of human cognition across all interaction modes
• Background in designing and testing multimodal systems