Staff Engineer (Application Pod)

Parspec•San Mateo, CA

1d•Hybrid

About The Position

Parspec is building the AI and digital infrastructure for the construction materials supply chain. Construction is a $15 trillion industry, yet the systems that underpin the buying and selling of materials remain fragmented, manual, and disconnected. Distributors and rep agencies rely on spreadsheets, PDFs, phone calls, and siloed tools to find new products and quote and manage projects; creating delays, errors, and margin erosion across the supply chain. Parspec is an AI-native platform that powers how construction products are discovered, bought, and sold. Trusted by more than 300 MEP distributors and rep agencies, Parspec helps project-driven businesses bid faster, win more work, and operate more profitably. By combining product intelligence, AI-powered workflows, and a connected ecosystem, Parspec is laying the foundation for a more intelligent, efficient construction supply chain. Founded in 2021 and headquartered in San Mateo, California, Parspec has raised $31 million from leading deep-tech and construction-technology investors. The Opportunity We are building a new kind of engineering org — small, AI-augmented, and shipping faster than teams three times our size. The model is working. Now we need someone who can see across the entire system and make it better in ways no single team can. As Staff Engineer, you operate at the level above individual teams. You own the technical problems that do not fit neatly into one domain — the ones that require understanding the full stack, the business model, and the long-term architecture at the same time. You work directly with leadership to shape what we build and how we build it. Your decisions outlast any single sprint. This is a role for someone who has already done the Lead thing, found it limiting, and wants to operate at greater scope. You are not here to run a team. You are here to raise the ceiling on what the entire engineering org can do.

Requirements

12+ years of full-stack engineering experience, with multiple cycles of building, scaling, and evolving production systems.
Has operated at Staff level or above — you know what it means to influence without authority, to own problems that do not have a clear team, and to make bets that play out over years.
Thinks in systems — you see how data models, API contracts, team boundaries, and product decisions interact, and you can hold all of it at once.
AI-native at scale — you have used AI tools deeply enough to have opinions about how they change system design, code review, and architectural decision-making, not just individual productivity.
Has made engineers around them significantly better — not by proximity, but by intention. You can point to leads and seniors who think differently because of you.
Comfortable operating in the white space — at this stage, the problems that need Staff-level thinking are often the ones nobody has fully defined yet. You define them.
Strong product and business mindset — you think beyond technical excellence and focus on what creates lasting value.

Nice To Haves

You have been Staff or Principal at a high-growth startup and shaped technical direction at the company level.
You have made an architectural call that turned out to be right two years later — and one that turned out to be wrong, and you know why.
You have a written point of view on how AI changes software architecture, not just developer tooling.
You have mentored leads, not just senior engineers.
You have owned a migration or re-architecture that touched every team and shipped without blowing up production.

Responsibilities

Own the hardest cross-org technical problems — the issues that span multiple domains, have no obvious owner, and get worse if nobody takes them. Data model integrity, service boundary decisions, performance at scale, security architecture.
Shape the technical strategy — be the primary engineering voice in conversations about where the platform is going, translating business direction into technical bets and technical reality into strategic constraints.
Ship — you are not emeritus. You still write code, own critical systems, and close the problems you identify. Influence without production footprint is just opinion.
Set the standard for AI-augmented development org-wide — define what “good” looks like when engineers work with Claude Code at scale. Build the patterns, document the traps, and raise the bar across every team.
Manage development across git worktrees and parallel agent sessions.
Review, refine, and validate AI-generated code to ensure correctness, performance, and maintainability.
Continuously improve development workflows using AI tools like Claude Code, CodeRabbit, and automated pipelines.
Drive architectural evolution — see the 18-month arc of what we need to build now versus what will need to change as the org scales. Write the ADRs that will matter in two years.
Ensure all systems meet quality standards through automated testing, AI-assisted code review, and hands-on validation.
Act as the ultimate quality bar for the most critical systems and architectural decisions.
Make leads better — your primary leverage is through the engineers who lead domains. Review their architecture decisions before they are made, not after. Give feedback that shifts their mental models, not just their PRs.
Review AI-generated and cross-functional PRs for the most critical systems and architectural boundaries.
Work with engineering leadership and product to challenge assumptions, refine strategy, and improve solutions.
Define best practices for AI-assisted development, testing, and code review at the organizational level.
Create and maintain CLAUDE.md files and development conventions that scale across every team.
Establish the architectural patterns and engineering playbook that the entire org operates from. Your standards outlast any single sprint.