Advance human-AI collaboration by engineering solutions that extend human capabilities and ensure efficient, safe, and intuitive interactions across multiple modalities.
Requirements
- Deep expertise in cognitive load and modality management, human error analysis and mitigation, decision-making under uncertainty, and human–automation interaction.
- Experience evaluating multimodal AI/ML systems (voice, NLP, data visualization, multimodal agents).
- Scientist–engineer hybrid with the ability to analyze human decision-making across voice, text, and visual modalities.
- Skilled at translating cognitive principles into engineering requirements for multimodal AI systems.
- Experience designing and testing multimodal systems to ensure optimal human cognition across all interaction modes.
Responsibilities
- Conduct cognitive task analyses for multimodal workflows, including voice, chat, visual dashboards, and ambient signals.
- Translate cognitive 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).
- Design and evaluate cross-modal decision support, determining when an AI should “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.
- Analyze, model, and design human–AI systems that reflect how people think and perform in complex environments.
Other
- Advanced degree in Cognitive Engineering, Human Factors, Applied Cognitive Psychology, Systems Engineering, or a related field.
- Proven experience in complex, high-stakes domains requiring advanced engagement.
- Collaborate with product, design, and engineering teams to ensure multimodal systems are grounded in cognitive principles.
