Principal Software Engineer - AI Native UX

What you'll do: UX Architecture & Strategy — Define and own the long-term architecture for AI-native user experiences across the platform, making critical build-vs-buy decisions (design systems, agent-to-UI interaction patterns, real-time/streaming UX for AI-generated content) to ensure the experience layer stays resilient and coherent as the platform scales.

AI-Native Intelligent Experiences — Design and build user-facing surfaces where agentic backends - recommendations, autonomous triage, generated fixes/insights, multi-step agent reasoning - are exposed to end users through fast, trustworthy, low-friction interaction patterns.

Own the hard UX problems unique to AI-native products: progressive disclosure of agent reasoning, graceful handling of uncertainty/latency, human-in-the-loop review flows, and making autonomous system behavior legible and controllable to end users rather than a black box.

UXC & Workspace Platform — Deep ownership of the UXC (User Experience/Workspace, UI16) surface — component architecture, design system consistency, and performance — as the primary canvas AI-native features are built on top of.

Agentic Pipeline Awareness — Partner closely with platform/ML teams building the agent orchestration and tool-calling infrastructure underneath (multi-agent dispatch, MCP-style tool integration, reasoning traceability) — translating what these backends can do into UX patterns that make them usable, safe, and trustworthy for real users.

Business Alignment — Align UX and technical architecture with business goals, ensuring engineering velocity, experience quality, and adoption of AI-native features directly support customer acquisition, retention, and time-to-market.

Hands-on Engineering — Write high-performance, clean, production-grade code primarily across the frontend/experience stack (Lit.js, React/TypeScript, design systems, component architecture), with working fluency in backend/platform code (Java, Python, Postgres, K8s) to collaborate effectively with platform teams.

Cross-functional Execution — Drive business-critical UX and platform outcomes in close collaboration with ML, backend platform, and product teams.

Engineering Culture — Mentor senior engineers, champion UX and engineering excellence, and foster a culture of rapid, impact-driven innovation in AI-native product development.

Ownership & Initiative — Proactively identify UX and platform gaps in the AI-native experience, propose solutions, align stakeholders, and drive execution with a high degree of ownership.

Experience leveraging or critically thinking about how to integrate AI into engineering and platform work—AI-powered tooling, automated operational workflows, agentic systems for fleet visibility and operations, or reasoning about AI’s impact on how infrastructure is built and run. 15+ years of software engineering experience with demonstrated progression into senior technical leadership, and a track record concentrated in user experience and frontend platform engineering .

Deep UXC / AI-native UX expertise - has shipped intelligent, AI-driven user experiences (not just consumed an LLM API), with strong intuition for how agent/model output should surface to real users.

Hands-on experience with agentic/multi-agent systems from the consumer side — understands agent orchestration, tool-calling protocols (MCP or equivalent), and reasoning traceability well enough to design UX around them, even if platform-layer implementation sits with another team.

Strong hands-on coding ability with deep design thinking in the frontend/experience stack (React/TypeScript, component/design systems), working proficiency in backend fundamentals (K8s, Java, Python, Postgres) to bridge into platform architecture conversations.

Demonstrated ability to connect UX and engineering decisions to business outcomes - adoption, retention, time-to-market, not just technical metrics.

Self-driven with strong ownership, able to identify UX and system gaps, propose solutions, align stakeholders, and execute at startup pace.

Experience integrating or critically evaluating AI tools/agentic workflows in product and engineering decision-making, particularly from a user-experience and trust/legibility angle.

Good to Have Prior experience in distributed systems performance/scalability, even if not the primary focus of recent roles.

Open-source contributions to major AI-native UX, design-system, or agent-framework projects.

Prior startup experience working closely with Product and Growth teams in an early-stage environment.