Staff Software Engineer

What You’ll Do
🏗️ Strategic Architecture & Long-Term Vision
Define the Future: Own the long-term product and technical vision for the AI agent and the vertical applications it powers, anticipating the scale, security, and product needs of LLM-powered robot programming a year or more out, including its core representation problem: how a language model safely reads, writes, and edits large structured automation programs.
Design the Experience: Own how programming a robot through conversation should feel: the interactions, defaults, and guardrails that earn an operator’s trust.
Decouple and Scale: Design the agent’s tool surface as clear, stable interfaces against our robot platform APIs, so that what the agent can do grows with the platform, not against it.
Standard Setter: Set the engineering standards for a nondeterministic product: latency and token budgets as product requirements, and evaluation gates as the bar every release must clear.

🏎️ Complex Project Leadership & Execution
Product Ownership: Act as part-PM for your surface: work backwards from the product experience, spend real time with customers and operators, be opinionated on the roadmap, and own outcomes, not tickets.
Build the Verticals: Ship vertical applications alongside the agent: purpose-built flows for palletizing, welding, and machine tending that encode how the work is really done, and that the agent can drive end-to-end.
Drive the Program: Lead the agent’s path from demo to release across our robot platform, QA, product, and AI teams: convert fast-moving asks into testable requirements and hold scope against a real ship date.
Hands-on Delivery: Own production hardening end-to-end across the agent’s TypeScript/Node stack: reliability, secure key management and usage controls, degraded/offline modes, and review- and QA-gated release engineering.

🛠️ Production Excellence & Organizational Leverage
Systemic Quality: Build the agent’s evaluation discipline (datasets mined from real usage, regression gates in CI, model-graded scoring) so that every early-customer failure, in any vertical, becomes an eval case and every eval win becomes a release decision.
Technical Multiplier: Mentor and coach the engineers around you, significantly contributing to their technical growth and to the quality of everything the team ships.
Influence & Accountability: Bring rigor to how the company understands agent quality: shared metrics that leadership, product, and customers can trust.

Who You Are
We are looking for a product-minded, LLM-native engineer who has shipped AI to real users and gets excited about both the agent and the real-world automation tasks it will drive, like palletizing and welding. You want to understand how the work actually gets done on a factory floor, and you build for the people doing it. This is not a model-training research role, and no robotics background is required. Agentic coding tools should already be part of your default workflow.

Skills You’ll Bring
Experience: 8+ years of professional software engineering, including hands-on AI work: shipping an LLM-powered product (agent, copilot, AI-native workflow), building model evaluation systems, or production data work for AI/ML. Ownership of an LLM product end-to-end is the strongest fit.
LLM Product Engineering: Fluency in agent loops and tool calling, prompt and context management, structured output, model selection and fallbacks, and token/latency economics as engineering constraints.
Evaluation & Quality: You’ve built eval datasets, harnesses, or regression gates for nondeterministic systems and used them to make real launch decisions.
Product Thinking: You’ve owned what to build, not just how to build it: you talk directly to users, form sharp opinions about their problems, and put decisions in writing. Experience serving operational, domain-heavy users (manufacturing, logistics, field operations) is a strong plus.
Design Sensibility: Strong opinions about user experience, held loosely: you prototype interactions before hardening them, sweat the details users actually feel, and partner naturally with design.
Excelling in Ambitious Environments: Your best work happened where the bar was public, leadership was close to the product, and the plan changed weekly.
Tech Stack Expertise: Fluency in TypeScript/Node.js is preferred, with range across our core stack (React, Python, C++, Kubernetes).