About the job
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.
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
Qualifications
Minimum
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
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