Agentic Llms Engineer Jobs

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Today

Staff AI Engineer

Pulley San Francisco, CA

San Francisco, CA

Today

$300k - $350k/yr Full time Agentic Llms Artificial Intelligence Cloud

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Job Description

Lead AI engineering to bring LLM-powered intelligence to permitting workflows, with end-to-end ownership from problem framing to production evaluation.

Responsibilities

Own the AI problem space by setting the technical direction for how Pulley applies LLMs across multiple product surfaces, from ambiguity to architecture to shipped, iterated outcomes
Convert permitting documents, city regulations, and jurisdiction workflows into structured, reliable outputs, including extraction, classification, retrieval, and agentic workflows over document sources not designed for machine reading
Define the company evaluation and observability standard: establish ground truth, measure quality and regressions, and implement systems that make “model improvement” measurable and default for LLM feature teams
Use AI agents as a daily practice by directing, reviewing, and shipping agent-driven work at high velocity while maintaining a clear quality bar
Make long-range technical bets that shape what Pulley can build next year, including model and architecture choices and decisions to build versus buy, with ownership of production consequences
Multiply engineering impact by establishing reusable patterns for LLM feature development, mentoring senior engineers toward larger scope, and improving team speed through systems, standards, and abstractions

Requirements

8+ years of software engineering experience, with a substantial portion focused on production LLM or ML systems
Track record owning a significant AI domain end-to-end, including problem identification, architecture, delivery, and production ownership, plus non-glamorous requirements such as data quality, evaluation design, cost and latency, and failure handling
Hands-on, production experience with large language models, including prompting, retrieval-augmented generation, structured extraction, tool use, and agentic workflows, with judgment about when each approach is the wrong tool
Experience designing evals and making LLM-powered features reliable in production
Experience building with AI coding agents where agents performed substantial implementation under your direction (not limited to autocomplete)
Ability to architect durable systems with pragmatic tradeoffs
Experience mentoring engineers and/or setting technical direction that other engineers delivered against
Based in the San Francisco Bay Area and willing to work in person 4 days a week

Technologies

LLMs
ML
Retrieval-augmented generation
TypeScript
React
Google Cloud

Compensation

USD 300,000 - 350,000 per year
Offers Equity

Employment Details

Employment type: Full time
Department: Engineering
Location: San Francisco, CA (hybrid)

Who You Are

Thrives in ambiguity, preferring to define the right problem over executing a prewritten spec
Product-minded, focused on whether solutions solve customer needs and willing to talk to users
Rigorous about what “working” means, prioritizing evals over demos and building measurement before building features
Strong opinions about balancing quality and velocity using the right tools, abstractions, and processes
Defaults to organizational-scale ownership, taking initiative to fix missing systems, processes, or gaps without needing permission

Nice to Have

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 evaluation sets from real-world usage
Experience in construction tech, govtech, proptech, or other domains where the hardest work involves messy real-world documents and processes
Experience with modern full-stack development, including working in application code that puts AI features in front of users (TypeScript, React, Google Cloud)
Experience serving as the most senior AI engineer in a domain, the escalation point when others need answers
Startup experience at the stage where you helped build the team, not only the product

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