Staff Software Engineer - AI-Native Development & Agent Orchestration

We’re hiring a Staff Software Engineer who thinks in systems, not just stories. Someone who’d rather spend an hour teaching an agent to handle a class of work forever than spend that hour doing it once. If your instinct is to remove the bottleneck instead of working around it, keep reading.

The Role

You’ll sit at the intersection of product engineering, AI infrastructure, and technical leadership - owning the machinery that makes autonomous agent workflows reliable enough for Fortune 100 customers.

Core Responsibilities

• Architect agent-ready systems. Design the context, tooling, evaluation harnesses, and guardrails that allow coding agents to execute full development workflows - from plan through merged release - with decreasing human intervention over time.
• Orchestrate parallel agent fleets. Delegate work across multiple agents simultaneously, inspect results against enterprise quality standards, and when an agent stumbles, improve the underlying capability so the fix is permanent and shared.
• Ship production features fast. Work in a closed loop with product leadership where customer feedback captured in the morning can be live by afternoon. Speed matters, but never at the expense of reliability - our customers’ brand reputations depend on what we ship.
• Raise the ceiling for everyone. Package every improvement - rules, skills, context files, eval frameworks - so the entire team benefits. Your leverage compounds across people, not just your own output.
• Lead senior engineers. Scope work, review architecture and pull requests, and hold the quality bar. You set technical direction and model the AI-native way of working for others., * Unlimited AI budget. No token caps, no tooling restrictions. If a better model or tool exists, use it - that conversation is always open.
• Fully remote, async-first, global. Deep work on your schedule; written artifacts over meetings; fast feedback loops over slow ceremonies.
• Compounding career growth. You’ll develop expertise in the discipline that matters most going forward: building the leverage layer that lets AI own more of the work, cycle after cycle.
• Startup speed, enterprise scale. Weekly outcome cycles, Fortune 100 customers, and a lean team where your decisions have outsized impact.

If you’re ready to stop treating AI as an add-on and start engineering the system that makes autonomous software delivery real, we’d like to talk.

• 5+ years of full-stack web engineering with genuine depth on both front-end and back-end, plus a proven track record as a tech lead directing senior and mid-level engineers.
• AI-native operating style. You don’t hand-write code line by line or pair 1:1 with a chatbot. You build the scaffolding - prompts, context, rules, evals - that lets agents deliver autonomously, and you verify the output meets enterprise standards.
• Frontier-tool fluency. Power-user command of today’s top agentic coding tools (Claude Code, Cursor, Codex, or equivalents) with genuine curiosity to evaluate alternatives weekly. Loyalty to one tool at the expense of awareness is a disqualifier.
• Strong model intuition. You can articulate which frontier model fits which task, what shifted in the latest releases, and when to optimize for capability versus cost.
• Product sense and ownership. Given a goal - not a spec - you figure out the right solution, ship it polished, and treat “merged” as the midpoint, not the finish line. Enterprise quality (defensive coding, edge-case coverage, security, automated evals) is your default.
• Cloud & CI/CD self-sufficiency. Solid AWS experience; you can stand up, ship, and operate a product end-to-end without handing off infrastructure.
• Production LLM integration. You’ve shipped real applications powered by LLMs - APIs, prompt engineering, agents, or automation pipelines.
• Model Context Protocol (MCP) understanding and hands-on experience.

Nice to Have

• Background in enterprise community platforms, social messaging, or customer-engagement software.
• Experience designing multi-agent or orchestrator/sub-agent workflows with parallel execution.
• Published work or open-source contributions in agentic systems or AI-assisted engineering.
• History of building shared internal tooling that measurably lifted an entire team’s throughput.