Senior Associate-Cognitive AI Engineer
This role focuses on designing AI systems that bridge human cognition with multimodal interactions, requiring deep expertise in translating cognitive insights into scalable, intuitive agent architectures. The engineer will lead the development of decision-support tools that integrate voice, text, and visual interfaces while ensuring seamless human-AI collaboration in complex workflows.
Skills required
About this role
Are you passionate about advancing human–AI collaboration? Join our Transformative AI team as a Cognitive Engineer and help shape the future of multimodal systems. In this role, you’ll engineer solutions that extend human capabilities, ensuring interactions are efficient, safe, and intuitive across multiple modalities.
As a Senior Associate-Cognitive AI Engineer (Multimodal Systems) in the Transformative AI team, you will analyze, model, and design human–AI systems that reflect how people think and perform in complex environments. You will focus on optimizing decision-making, information flows, and human–agent interactions across voice, text, data visualization, and ambient interfaces.
Job 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).
- Collaborate with product, design, and engineering teams to ensure multimodal systems are grounded in cognitive principles.
- 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.
Required qualifications, capabilities, and skills:
- 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.
- 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).
Preferred qualifications, capabilities, and skills:
- 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.
