Vice President-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 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 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

Required qualifications, capabilities, and skills:


* 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)

Preferred qualifications, capabilities, and skills:


* 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

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