Job description

About Pulley Pulley helps the country’s top architects, builders, and retailers speed up every project in their portfolio. With AI-powered permitting intelligence and expert guidance, we eliminate costly delays and bring predictability across the full lifecycle of commercial projects. Today, permitting is the slowest, most uncertain part of building, spread across 19,000+ jurisdictions with different rules, timelines, and surprises. Pulley gives project teams the clarity and predictability they need to move from planning to opening without delays. We support rollout programs for brands like J.Crew, Solidcore, and Hibbett Sports, as well as major data center buildouts, EV charging networks, and other commercial projects. Our platform dramatically reduces approval timelines, improves forecasting accuracy, and removes thousands of hours of manual work from design and construction teams. Founded in 2021, Pulley combines deep permitting expertise with purpose-built AI from people who have created products used by millions. We’re backed by CRV, Susa Ventures, Fifth Wall, and leaders from Plaid, Segment, ServiceTitan, and Procore. WHAT YOU’LL DO 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 senior-level AI engineer, you will: - Own AI-powered features end-to-end—from talking to users and defining what “correct” means for a permitting workflow, through prompt and pipeline design, evals, deployment, and iteration in production - 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 - Build the evaluation and observability foundation that lets us ship LLM-powered features with confidence: define ground truth, measure quality and regressions, and know when a model change is actually an improvement - Build with AI agents as a daily practice—directing, reviewing, and shipping agent-driven work at high velocity while owning the quality bar - Make technical and product decisions that have direct impact on our customers and their projects - Raise the bar for the engineers around you in how they build with LLMs, through design review, mentorship, and the standards you set in your own work WHO YOU ARE - 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: when something is broken or missing, your instinct is to fix it, not to file it as someone else’s problem NEED TO HAVE - 4+ years of software engineering experience, with a substantial portion building production LLM or ML systems - Track record of owning an LLM-powered product surface end-to-end: requirements through production, 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 - 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 - Startup experience at the stage where you helped build the team, not just the product - Experience mentoring engineers or leading technical direction across teams

Requirements

Must-haves

Own AI-powered features end-to-end—from talking to users and defining what “correct” means for a permitting workflow, through prompt and pipeline design, evals, deployment, and iteration in production
Build the evaluation and observability foundation that lets us ship LLM-powered features with confidence: define ground truth, measure quality and regressions, and know when a model change is actually an improvement
Build with AI agents as a daily practice—directing, reviewing, and shipping agent-driven work at high velocity while owning the quality bar
Make technical and product decisions that have direct impact on our customers and their projects
Raise the bar for the engineers around you in how they build with LLMs, through design review, mentorship, and the standards you set in your own work
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: when something is broken or missing, your instinct is to fix it, not to file it as someone else’s problem
4+ years of software engineering experience, with a substantial portion building production LLM or ML systems
Track record of owning an LLM-powered product surface end-to-end: requirements through production, 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
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
Startup experience at the stage where you helped build the team, not just the product
Experience mentoring engineers or leading technical direction across teams

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmltypescriptreact

Platform: ashby
Effort: quick

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