Staff Software Engineer

• Adversarial Verification Pipeline. Design and scale the systems that test AI models against reality not benchmarks. You'll build the evaluation framework that cross-references marketing claims against specs reviews human network signals and live transaction data. When an LLM says a product is "best in class" your pipeline determines whether that claim survives contact with evidence.
• Agent-Facing Protocol Layer. Ship the infrastructure that lets third-party AI systems consume verified commerce intelligence programmatically. REST APIs JSON-LD structured data MCP Server tools. You'll own the interface design between human-centered commerce and agent-centered commerce - where our defensive moat becomes an offensive revenue line.
• Knowledge Graph Infrastructure. Own the storage and query layer serving 75M+ product entities to three audiences simultaneously: humans (HTML) agents (JSON-LD) and internal systems (raw API). Same source of truth polymorphic output sub-200ms p99 latency. You'll decide how verified knowledge is structured versioned and served at scale - and how it degrades gracefully when upstream signals conflict.
• Evaluation Infrastructure. Build the machinery that runs continuous red-team verification against our own knowledge systems. Automated adversarial testing human network signal integration statistical rigor applied to confidence scoring. This is the internal immune system that catches our mistakes before users do.
• Foundational Architecture. Make the right calls on data models services queues and caches across the entire stack. At a 20-person company the staff engineer's architectural taste determines whether the team ships at 2x or 0.5x. Choose complexity only when complexity is earned.

• You have shipped AI to production and stayed to maintain it. You understand that evaluation and verification are harder than model training. Your opinions about RAG architectures fine-tuning tradeoffs and agentic system design are grounded in production incidents not arxiv papers. You know the difference between a demo that impresses a boardroom and a system that serves real users without hallucinating.
• You think in systems not features. You spot where a technical choice in the data model will compound into a philosophical problem in the product six months from now. You ask "what happens when this is wrong?" before you ask "how do we make this faster?" You build from first principles not borrowed "best practices" from companies solving fundamentally different problems at fundamentally different scale.
• You are a builder who happens to be senior not a senior who has stopped building. You write production code not just architecture diagrams. At a 20-person company the staff engineer reviews pull requests AND submits them. You are comfortable with ambiguity and you close it through iteration not through longer planning cycles.
• You use AI to build AI. You have wired LLMs into your development workflow as force multipliers. You have a clear-eyed view of what they're good for (boilerplate iteration speed pattern matching) and where they fail (architectural decisions novel tradeoffs edge case reasoning). You are practical not ideological.
• You value signal over process. You measure your work by what runs in production not what lives in a design doc. You optimize for reality - does the code serve real users? - not internal metrics like coverage percentages or sprint velocity.

• Base: $350000 - $450000. Top of market. Your base covers your life; your ownership builds your freedom.
• Equity: You receive a formal ownership stake via Profits Interest Units (PIUs) - Class B Membership Interests with a $0 strike price. Not options. Not RSUs. Actual ownership that participates in the upside from day one and qualifies for Capital Gains tax treatment.
• Profit Sharing: You participate in the company's annual success. As an equity holder you receive a pro-rata share of Free Cash Flow (FCF) - real cash every year not a promise tied to a future exit.
• Liquidity: We operate an Annual Tender Offer where the company buys back vested interests at fair market value. You can turn ownership into cash every year. No waiting for an IPO. No hoping for an acquisition.
• Benefits: 100% premium coverage for you and your family daily catered lunches and unlimited PTO that we actually expect you to use.

What We Do

Product.ai (formerly Demand.io) is the truth layer for commerce. Built on Axiomatic Intelligence — a proprietary adversarial reasoning methodology that stress-tests product claims against physics economics and engineering constraints — Product.ai delivers verified purchase verdicts not summaries. Product.ai tells consumers when NOT to buy. Product.ai emerges from Demand.io a profitable bootstrapped AI commerce company whose SimplyCodes platform processes over $1B in annual transaction value with a team of 20. Founded by Michael Quoc.

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