We’re seeking a UX Engineer who thinks beyond chatbots and conventional AI interfaces. This role is for someone who wants to fundamentally rethink how users interact with complex operational and analytical systems when those interactions are mediated through AI.

You’ll combine deep UX expertise with practical AI implementation skills to create thoughtful, novel interfaces that make sophisticated data, recommendations, and decision-making workflows intuitive and actionable.

This isn’t about adding a chat widget to an existing product. It’s about exploring how AI can bridge the gap between sophisticated analytics and real-world operational decision-making. What does decision support look like when systems understand user context and goals? How do we surface insights that align with how people naturally think and work? What new interaction paradigms emerge when multiple streams of complex information converge into intuitive guidance?

What You’ll Do

• Design and prototype AI-native interaction patterns that extend beyond conversational interfaces—exploring multimodal inputs, contextual awareness, adaptive interfaces, and new information architectures
• Build production UX implementations for AI-mediated experiences, including prompt engineering, context management, real-time model interactions, and resilient fallback patterns
• Translate complex model behaviors into intuitive, predictable user experiences that balance capability with trust and usability
• Conduct rapid experimentation with emerging AI capabilities, prototyping novel interaction models and validating them through user feedback
• Establish reusable patterns and frameworks for AI-native experiences that broader teams can build upon
• Help shape product direction by identifying opportunities where AI can fundamentally improve user experiences—not simply automate existing workflows

• Demonstrated excellence in traditional UX design, including information architecture, interaction design, visual hierarchy, usability, and accessibility
• Strong frontend development skills with modern technologies (React, TypeScript, or similar) and the ability to build production-ready interfaces
• Portfolio demonstrating thoughtful, user-centered design across web and/or native experiences

• Hands-on experience building interfaces that integrate LLMs or other AI capabilities—not just consuming APIs, but thoughtfully architecting how AI fits into the user experience
• Understanding of prompt engineering, context management, streaming interactions, and model behavior patterns
• Practical understanding of AI capabilities and limitations, with the ability to design effectively within constraints while pushing boundaries
• Experience with agentic systems, multi-step interactions, tool orchestration, or advanced AI workflows is a strong plus

• Intellectual curiosity about the evolving space of human-AI interaction, with an interest in emerging research, tooling, and interaction models
• Comfort with ambiguity and rapid iteration in a space where best practices are still evolving
• Systems thinking—the ability to understand how models, user behavior, interface design, and business objectives intersect
• Strong communication skills and the ability to explain design rationale across engineering, product, and research teams

• Experience with local or edge AI inference and an understanding of the tradeoffs of running models outside traditional cloud environments
• Background in human-computer interaction, cognitive science, behavioral design, or related disciplines
• Familiarity with accessibility considerations unique to AI interfaces, including streaming content and probabilistic experiences
• Open-source contributions or publicly available work demonstrating novel AI interaction approaches
• Experience in industries where AI is actively reshaping workflows, such as productivity software, technical tools, research platforms, or operational systems

• Designing systems that proactively surface recommendations based on changing contextual signals, historical patterns, and user priorities—determining when to notify users, how much information to present, and how to preserve user agency
• Building trust in AI-generated recommendations by helping users understand confidence, uncertainty, and reasoning in ways that feel intuitive and transparent
• Prototyping interfaces that learn user preferences and workflows through interaction rather than requiring heavy upfront configuration
• Designing multimodal experiences that synthesize complex inputs into clear, actionable decisions aligned with how users naturally operate in their day-to-day environments