AI Product Designer

About the Role You'll pioneer interaction patterns for AI systems that don't yet have established playbooks. This role sits at the frontier of product design — crafting interfaces for LLM-powered features where you must design for unpredictability, conversational flows, and building user trust in AI-generated outputs. You'll work directly with ML engineers and researchers in San Jose to translate cutting-edge model capabilities into intuitive user experiences, solving hard problems like designing around hallucinations, variable latency, and emergent AI behaviors. The AI product landscape shifts weekly; you'll thrive in ambiguity, rapid iteration, and defining best practices as you go. Our Stack - Figma as the primary design tool, with emphasis on component-driven design and collaboration with engineering on design tokens and implementation fidelity - Cutting-edge AI/ML stack — you'll work alongside teams building with LLMs, vector databases, and real-time inference systems; understanding these constraints shapes your design decisions - Rapid prototyping culture — InVision, Framer, or code prototypes to test conversational flows, progressive disclosure patterns, and AI-specific interaction models - Modern product analytics and research tools (Maze, Dovetail, Amplitude) to measure how users actually interact with AI features and where trust breaks down What You'll Do - Design end-to-end interaction flows for generative AI features — chat interfaces, prompt experiences, AI-assisted workflows — balancing model capabilities with user mental models and trust-building patterns - Prototype novel UI paradigms for LLM interactions using Figma and code-based prototyping tools, testing hypotheses about how users learn to collaborate with AI systems - Analyze user research data and usage patterns to identify friction points in AI-generated outputs, then propose evidence-based design solutions that account for model constraints and probabilistic behavior - Partner closely with ML engineers to understand prompt engineering, model limitations, and latency characteristics, translating technical constraints into elegant user experiences - Establish design system components and interaction guidelines for AI features, creating reusable patterns as this product category matures - Drive design critiques and share learnings across the product team, building institutional knowledge about what works (and what fails) in AI UX - Refine micro-interactions and visual feedback mechanisms that communicate AI confidence levels, processing states, and error recovery — details that determine whether users trust the system What We're Looking For - 3-5 years of product design experience, with at least 1-2 years designing for AI/ML products, complex data-driven interfaces, or other highly technical domains where user understanding is critical - Expert proficiency in Figma for designing production-ready interfaces, component libraries, and rapid prototyping — you've built and maintained design systems, not just mockups - Strong interaction design skills with portfolio examples demonstrating how you've designed for unpredictable systems, complex workflows, or interfaces where users need to build mental models of non-obvious behavior - User research fluency — you can design and conduct studies to understand how users form trust in AI-generated outputs, what they do when the system fails, and how they learn new interaction paradigms - Analytical thinking applied to design decisions — you use both qualitative insights and quantitative metrics to validate whether your UX decisions actually help users accomplish their goals - Comfortable working in extreme ambiguity — AI product design has few established patterns; you thrive when the right answer is "let's prototype three approaches and test them" - Portfolio demonstrating AI/ML product work, complex system design, or innovation in interaction patterns Nice to Have - Experience with prompt engineering, LLM capabilities/constraints, or direct collaboration with ML engineers to translate model behavior into user-facing features - Familiarity with prototyping tools beyond Figma (Framer, Principle, or code-based prototyping) to quickly test conversational UX, latency states, or multi-step AI interactions - Background in designing for trust, transparency, or explainability in AI systems, financial products, healthcare tools, or other domains where users need to understand "why" Bonus Points - Published writing, conference talks, or case studies about designing for AI/ML products - Experience contributing to or maintaining open-source design systems - Basic understanding of how transformer models, retrieval systems, or fine-tuning work — enough to have informed conversations with ML engineers