Surface Pattern Design Jobs in Boston, MA (NOW HIRING)

Responsibilities

AI EXPERIENCE DESIGN & INTERACTION PATTERNS

AI-NATIVE PROTOTYPING & DESIGN TOOLING

PARTICIPANT & ADVISOR RESEARCH

DESIGN SYSTEM & ACCESSIBILITY

POD COLLABORATION & PRODUCT INFLUENCE

Experience

8+ years of product design experience with at least 3 years designing AI-powered features in production consumer or professional-facing applications - not just design concepts, but shipped AI experiences with real users.

Demonstrated experience at Lead or Staff Designer level: defining design patterns used by others, contributing to product strategy, and setting the design quality standard for a product area rather than executing within one already defined.

Experience in financial services, fintech, or a regulated consumer product environment where compliance requirements shape design decisions in the same artifacts as user experience decisions.

Portfolio demonstrating AI experience design specifically: conversational interfaces, AI recommendation surfaces, uncertainty and error states, human-in-the-loop design patterns, and explainability UI - not just general product design work.

Design Skills - Required

Figma (expert level): component-based design, auto-layout, variant systems, prototyping, and the design system contribution workflow - at the level where the Lead AI Designer is extending and maintaining the component library, not just consuming it.

Interaction design for AI: proven ability to design for non-deterministic, probabilistic outputs; experience designing streaming text interfaces, confidence indicators, correction affordances, and AI attribution patterns in production products.

Conversational and agentic UI design: experience designing chat interfaces, voice interaction flows, or multi-step AI-guided task completion - with specific attention to how the design handles LLM response latency, variability, and failure.

Prototyping for AI: experience using Protopie, Voiceflow, or equivalent tools to prototype AI interaction patterns; ability to build lightweight front-end prototypes for research using AI coding tools.

User research: ability to design, recruit for, moderate, and synthesize qualitative research sessions specifically about AI trust, transparency, and comprehension; experience with usability testing of AI features, not just static interfaces.

Accessibility engineering: WCAG 2.2 AA compliance in practice, with specific experience designing for the accessibility challenges of AI-generated and dynamically updated content; familiarity with screen reader behavior for conversational and streaming UI patterns.

Design systems: experience contributing to and maintaining a shared component library at scale; ability to document patterns in a form that other designers and engineers can implement consistently.

AI Tooling - Required

AI-assisted design workflow in daily practice: generative AI tools for ideation and asset exploration, LLM-assisted copy drafting for UI text and disclosure language, and AI-powered design analysis - not as experiments but as standard working tools.

Prompt engineering familiarity: sufficient understanding of how LLM prompts shape outputs to contribute meaningfully to the prompt refinement process alongside the Lead AI Engineer - specifically for the copy, format, and disclosure requirements that are design decisions.

AI coding tools for prototyping: Claude Code, GitHub Copilot, Cursor, or equivalent; ability to build a lightweight React/TypeScript front-end prototype for research purposes without requiring engineering support.

Fluency in AI system concepts: understanding of RAG retrieval, streaming inference, confidence scoring, agent tool use, and model output variability at the level required to design for them - not to build them, but to anticipate their design implications accurately.