Melissa Tang — Product Designer
Core Alignment: AI-native UX designer with 11 years of experience (8 at Google) specializing in complex developer tools and AI-powered experiences. Expert at mapping UX architecture directly to foundation model constraints and prototyping in code.
Core Competencies & Proven Technical Impact
AI-Native Workflows & System Architecture
Led end-to-end design for agentic workflows, machine learning platforms, and lifecycle management surfaces — providing clarity and delivering significant impact across launches.
Architected context-aware assistants, semantic entity summaries, and native panel-driven design language systems optimized specifically for multi-turn AI interactions and low-latency complex troubleshooting workflows.
Pioneered novel canvas and component primitives for AI-powered journeys, tailored to continuous, multi-turn LLM agent execution.
Developer Tools & Complex Workflow Empathy
Deeply familiar with expert personas, complex workflows, and developer tools through extensive work on TFX onboarding (reducing setup from 6 hours to 1 hour), internal developer logging, and low-code platforms.
High-Velocity Product Delivery & Execution
Led end-to-end UX for a 0→1 automated machine learning platform under Google Brain through rapid beta-to-GA launch loops; bypasses prolonged alignment cycles by using localized prototyping to ship enterprise MVPs ahead of model iteration timelines.
Maintained absolute execution quality under tight enterprise deadlines, balancing granular layout accuracy in centralized Figma engineering systems with high-velocity product delivery across highly sophisticated domains.
Cross-Functional Strategy & Product Validation
Operates symmetrically with Tech Leads, machine learning engineers, and core infrastructure teams while driving design systems, user research, and cross-functional alignment to cut through technical ambiguity.
Validates complex system design via structural product excellence programs, converting 52 Critical User Journey (CUJ) self-assessments and cross-functional machine learning interpretability inputs into hard infrastructure requirements.
Education
M.S. in Human-Computer Interaction/design (HCI/d) — Indiana University (2015)
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