About SuperApp

AI was single-player. SuperApp makes it multiplayer.

SuperApp is building the AI collaboration super app—a shared workspace where people and leading AI models think, create, and make decisions together in real time. Instead of splitting communication, AI assistance, and content creation across separate tools, SuperApp brings group messaging, multi-model intelligence, and collaborative creation into one continuous experience.

Teams can move from an initial conversation to a finished document, presentation, spreadsheet, or interactive app without losing the sources, decisions, feedback, and context that shaped the work. SuperApp is multi-provider by design: people can choose, pin, or compare leading models, while the platform can route each step to the capability best suited to the task. (superapp.chat)

Role Overview

We are seeking a visionary systems-minded AI engineer to lead the design, architecture, and development of the Agent Harness. In this role, you will bridge the gap between advanced foundation models (LLMs) and reliable, production-grade execution.

You will not just be "vibe coding" prompts ; you will build the actual state machine, execution sandbox, tool-calling middleware, and security containment layers that allow AI agents to safely execute code, search the web, manipulate files, and query databases in real-time. This is a high-leverage role requiring deep expertise in distributed backend systems, runtime isolation, and stateful agent choreography.

What You’ll Do (Responsibilities)

Harness Architecture & State Management: Architect the next-generation execution runtime (Agent Harness) that drives SuperApp’s planning, reasoning, memory retention, and tool-execution loops. Build reliable state machines capable of pausing, resuming, and versioning agent trajectories.
Secure Sandbox Execution: Design and scale highly secure, isolated, and ephemeral environments (using Docker, gVisor, WebAssembly, or microVMs) to execute agent-generated code safely without putting host infrastructure at risk.
Tool & Integration Frameworks: Build and maintain high-throughput APIs and integration layers (such as the Model Context Protocol - MCP) that connect SuperApp to external services, databases, web search utilities, and complex file processors.
Security & Guardrail Engineering: Engineer robust system-level guardrails to detect and mitigate prompt injection, defend against jailbreak attempts, and ensure strict compliance with system prompt instructions and data isolation boundaries.
Performance & Context Optimization: Implement advanced routing, context window pruning, and context caching strategies to optimize LLM token usage, minimize round-trip latencies, and drastically reduce the operational cost of complex agent loops.
Technical Strategy & Mentorship: Write comprehensive high-level design documents, align cross-functional engineering teams on the technical roadmap, and mentor senior and mid-level software engineers across the organization.

About You

Experience: Minimum 6+ years of professional software engineering experience, with a proven track record of designing, scaling, and operating mission-critical distributed systems.
Agentic AI Expertise: 2+ years of production experience building or modifying agent execution environments, LLM orchestrators, or tool-calling frameworks (e.g., custom runtimes, LangChain, AutoGen, or similar systems).
Backend Mastery: Expert proficiency in Go and Python. You have a pragmatic, deep understanding of concurrent programming, microservices, gRPC, and highly scalable API design.
Runtime Isolation & Security: Strong experience with containerization and virtualization technologies (Docker, Kubernetes) and a solid grasp of sandboxing untrusted code execution.
Distributed Systems Fundamentals: Deep familiarity with message brokers (e.g., Kafka, RabbitMQ), caching infrastructure (Redis), and relational/non-relational database design.
Education: Bachelor’s or Master’s degree in Computer Science, engineering, or equivalent practical systems engineering experience.