Applied AI Engineer

Pulley; San Francisco (CA); On-site; USD 180,000 - 320,000; Full time

Job summary

Pulley is hiring a senior software engineer to own AI-powered features end-to-end for our permitting platform. You will shape how unstructured city documents become structured outputs and drive product decisions with real customer impact.

You’ll build evals and observability, design agent-driven workflows, and mentor peers as you raise the bar for quality and velocity in a fast-growing construction-tech company based in the San Francisco Bay Area, with in-person work four days a week.

Qualifications

• 4+ years of software engineering with production LLM/ML experience.
• Own end-to-end LLM-powered product surface, from requirements to production.
• Deep hands-on production LLM experience: prompting, retrieval, extraction, and agent workflows.
• Experience designing evals and ensuring reliability of LLM features.
• Experience building with AI coding agents under direction.

Responsibilities

• Own AI-powered features end-to-end—from user talks to production deployment.
• Turn unstructured permitting documents into structured outputs—extraction, classification, retrieval, and agentic workflows.
• Build evaluation and observability to measure quality and regressions.
• Develop with AI agents as a daily practice, shipping agent-driven work with high velocity.
• Make technical and product decisions that impact customers and projects.
• Raise the bar by mentoring engineers and setting standards for using LLMs.

Skills

LLM in production

Product ownership

Eval design

AI coding agents

System design

Ownership

Tools

TypeScript

React

Google Cloud

Job description

Open — Pulley has a posting for this job. Posted 23 Sep 2026.

About this job

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

• 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

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