Staff AI Engineer

Pulley • Seattle, WA

3d • $300,000 - $350,000 • Onsite

About The Position

In this role, you will build the intelligence behind the product that gets stuff built. Permitting runs on messy inputs—scanned plan sets, jurisdiction code, reviewer comments, application forms that differ in every city—and turning that into something fast, structured, and trustworthy is the core technical problem at Pulley. As a staff-level AI engineer, you will:
Own the AI problem space, not just features—define the technical direction for how Pulley applies LLMs across multiple product surfaces, and carry it from ambiguity through architecture to shipped, iterated-on product
Turn unstructured permitting documents, city regulations, and jurisdiction workflows into structured, reliable outputs—extraction, classification, retrieval, and agentic workflows over documents that were never designed to be machine-readable
Set the evaluation and observability standard for the company: decide how we define ground truth, measure quality and regressions, and know when a model change is actually an improvement—and build the systems that make that the default for every team shipping LLM features
Build with AI agents as a daily practice—directing, reviewing, and shipping agent-driven work at high velocity while owning the quality bar
Make the technical bets that determine what Pulley can build next year, not just this quarter—which models, which architectures, what we build versus buy—and own the consequences of those bets in production
Multiply the engineers around you: set the patterns others build LLM features within, mentor senior engineers toward larger scope, and make the whole team faster through the systems, standards, and abstractions you create

Requirements

• 8+ years of software engineering experience, with a substantial portion building production LLM or ML systems
• Track record of owning a significant AI domain end-to-end: from “this is somebody’s problem” through architecture, delivery, and production ownership, including the unglamorous parts—data quality, eval design, cost and latency, failure handling
• Deep hands-on experience with large language models in production—prompting, retrieval-augmented generation, structured extraction, tool use and agentic workflows, and knowing when each is the wrong tool
• Experience designing evals and otherwise making LLM-powered features reliable in production
• Real experience building with AI coding agents—not just autocomplete; you’ve shipped work where agents did substantial implementation under your direction
• Ability to architect durable systems while making pragmatic tradeoffs
• Experience mentoring engineers or setting technical direction that other engineers built within
• Based in the San Francisco Bay Area and willing to work in person 4 days a week

Nice To Haves

• Experience with document understanding at scale—OCR, layout-aware parsing, or vision-language models over scanned PDFs, drawings, or forms
• Experience fine-tuning models or building data pipelines to produce training and eval sets from real-world usage
• Experience in construction tech, govtech, proptech, or another domain where the hard part is messy real-world documents and processes
• Experience with modern full-stack development—we use TypeScript, React, and Google Cloud—and an appetite for working in the application code that puts AI features in front of users
• Experience as the most senior AI engineer in a domain—being the person others escalated to when nobody knew the answer
• Startup experience at the stage where you helped build the team, not just the product

Responsibilities

• Own the AI problem space, not just features—define the technical direction for how Pulley applies LLMs across multiple product surfaces, and carry it from ambiguity through architecture to shipped, iterated-on product
• Turn unstructured permitting documents, city regulations, and jurisdiction workflows into structured, reliable outputs—extraction, classification, retrieval, and agentic workflows over documents that were never designed to be machine-readable
• Set the evaluation and observability standard for the company: decide how we define ground truth, measure quality and regressions, and know when a model change is actually an improvement—and build the systems that make that the default for every team shipping LLM features
• Build with AI agents as a daily practice—directing, reviewing, and shipping agent-driven work at high velocity while owning the quality bar
• Make the technical bets that determine what Pulley can build next year, not just this quarter—which models, which architectures, what we build versus buy—and own the consequences of those bets in production
• Multiply the engineers around you: set the patterns others build LLM features within, mentor senior engineers toward larger scope, and make the whole team faster through the systems, standards, and abstractions you create

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed