AI Native - Full Stack Engineer

The Experience:

We are seeking a senior full-stack engineer with deep backend roots to build the next generation of application interfaces for an agent-driven world. Traditional UIs rely on static, step-by-step human workflows; our future surfaces rely on AI agents doing the bulk of the work while high-density dashboards give humans real-time visibility, intent control, and governance. To build interfaces that make complex, non-deterministic system state effortlessly clear, you must be someone who can write production agentic/AI code, reason deeply about distributed data architecture, and translate underlying backend complexity into intuitive, high-trust human experiences.

About the Role

We are looking for a rare software engineer: someone who understands backend application architecture and data pipeline engineering deeply, writes production backend and agentic code, and is equally driven by information design, front-end architecture, and human-computer interaction.

In an agentic world, control planes are shifting from rigid step-by-step workflows to intent-driven supervision. As AI agents execute complex, non-deterministic tasks, humans need clear visibility into application state, data lineage, and failure modes. You will own both the backend software logic and the visual control/telemetry surfaces—building the bridges that allow humans to direct, govern, observe, and intervene in AI-agent activities with high trust.

This is a hands-on, code-first software engineering role for someone who loves full-stack execution, agentic orchestrations, and transforming complex data flows and backend behavior into intuitive, powerful user interfaces.

What You’ll Do

• End-to-End Control Planes & Telemetry: Architect, write, and maintain backend services, domain models, APIs, and high-density front-end interfaces for intent expression, agent supervision, and anomaly diagnosis.
• Agentic Integration & Backend Execution: Design asynchronous workflows, event streams, and LLM/agent integration layers (tool-use, state machines, contextual memory) while ensuring resilience, auditability, and clear data provenance.
• Information Architecture & High-Density UI: Apply core information design principles (hierarchy, progressive disclosure, temporal tracking) to make non-deterministic system states and multi-agent interactions instantly legible through next-gen dashboards.
• Human-in-the-Loop Frameworks: Build the interaction logic, feedback loops, and state-reversal mechanisms for automated workflows. Implement dynamic guardrails, synchronous approval gates, and asynchronous audit trails based on risk and reversibility.
• Prototyping to Production: Rapidly code functional prototypes to test novel human-agent interaction models, stress-test them against backend performance constraints, and ship production-grade code across the full stack.

You’re Our Person If

• Full-Stack Software Engineering Experience: Strong backend foundation in web backend architecture, state management, asynchronous failure modes, and observability, alongside modern front-end engineering proficiency (e.g., React, TypeScript, modern state management, web visualization).
• Hands-on Agentic & AI Engineering: Practical experience writing code that orchestrates LLMs, AI agents, or tool-using model workflows (beyond basic prompt-engineering or chat interfaces).
• Innate Instincts for UI/UX & Information Design: A deep passion for human-computer interaction, data visualization, and micro-interactions. You care about friction vs. fluidity—knowing exactly when to require human intervention and how to clearly show causality and uncertainty.
• Product & Platform Thinking: Ability to reason through multi-agent governance, context delegation, and high-trust developer/operator tools.

Even Better If:

• Experience building developer tools, observability platforms, complex control planes, or investigative analytics software.
• A background or strong self-directed grounding in HCI (Human-Computer Interaction), design systems, or data visualization theory (e.g., Edward Tufte, Bret Victor, Tamara Munzner).
• Track record of building zero-to-one developer platforms or open-source tooling for AI agent supervision.