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# staff-level AI engineer

- **Company:** Pulley, LLC
- **Location:** Seattle, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Language Modeling, Software Engineering, TypeScript, Google Cloud, ReactJS, Retrieval-Augmented Generation, Large Language Models, Machine Learning Operations, Data Pipelines
- **Published:** September 22, 2026
- **Apply:** https://startup.jobs/staff-ai-engineer-pulley-10143959

## About the Role

* You thrive in ambiguity-you'd rather define the right problem than execute a spec, and you're energized rather than paralyzed when the path isn't laid out
 * You're product-minded: you care whether the thing you built actually solved the customer's problem, and you'll talk to users to find out
 * You're rigorous about what "working" means-you don't trust a demo, you trust an eval, and you build the measurement before you build the feature
 * You have strong opinions about quality and velocity and don't treat them as a tradeoff-you look for the tools, abstractions, and processes that buy both
 * You default to ownership at organizational scale: when something is broken or missing-a system, a process, a gap between teams-your instinct is to fix it, and you don't need permission or a mandate to start, * 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, * 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

## Description

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, * Experience with document understanding at scale-OCR, layout-aware parsing, or vision-language models over scanned PDFs, drawings, or forms


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