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 a 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.

Job responsibilities:

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 pipelinesModel 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 principlesDesign 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 teamingRun simulations and user-in-the-loop experiments to test system performance across modalities

Required qualifications, capabilities, and skills:

Formal training or certification in software engineering concepts and at least 5 years of applied experienceAdvanced degree in Cognitive Engineering, Human Factors, Applied Cognitive Psychology, Systems Engineering, or related fieldProven experience in complex, high-stakes domainsDeep expertise in cognitive load and modality management, human error analysis and mitigation, decision-making under uncertainty, human–automation interaction, and voice/visual trust calibrationExperience evaluating multimodal AI/ML systems (voice, NLP, data visualization, multimodal agents)

 

Preferred qualifications, capabilities, and skills:

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