Build and own an applied AI intelligence layer for construction documents. Set technical direction across computer vision, LLMs, and AI systems; select and coordinate detection, segmentation, OCR, and reasoning models; rapidly ship 0-to-1 prototypes; and harden them using user feedback. The role requires handling ambiguous problems, messy data, edge cases, and end-to-end production deployment as a founding engineer.
Founding Engineer — Applied AI (Computer Vision)
On behalf of an early-stage client · San Francisco, on-site · Full-time ·
Equity 0.20–1.20%
The build: Our client is building the
intelligence layer for construction — model-garden approach, newest computer
vision and LLM systems, shipped into prototypes users actually rely on. They're
an early-stage SF team, and they want a founding engineer to build it with
them.
This isn't a seat. It's a direction. As the founding
engineer, you'll set the technical direction, pick the models, and define how
the intelligence layer gets built. Expect ambiguity, ownership, and schlep —
and the equity to match.
What you'll do
- Own
the intelligence layer end to end — detection, understanding, and
estimation over construction documents.
- Set
the technical direction for how the team applies CV, LLM, and AI systems.
Model-garden it: pick, test, combine.
- Ship
0-to-1 prototypes fast, then harden them from real user feedback.
- Coordinate
models — detection, segmentation, OCR, LLM reasoning — into one coherent
system.
- Do the
schlep. Early product = messy data, weird edge cases, no clean pipeline,
no playbook.
Requirements
What we're looking for
- Applied
CV/ML in Python — deep learning, computer vision, object detection.
- 0-to-1
builder. You've started something, or joined at the start and
owned it.
- AI in
production — you've shipped, not just trained.
- Slope
> y-intercept. Fast learner beats a static résumé.
- Empathy
+ honesty about what's working and what isn't.
- Comfortable
with schlep and ambiguity — there's no one to hand it to.
Bonus
- You
built something people used or paid for (founder/startup).
Similar Jobs
Artificial Intelligence • Software • Biotech • Pharmaceutical • Generative AI
Build and operate production AI systems for FDA regulatory intelligence, including evaluation pipelines, document ingestion and retrieval, multi-agent workflows, data pipelines, cloud infrastructure, and secure multi-tenant services. Collaborate with former FDA experts to translate domain judgment into product requirements and evaluation cases. Own architecture, reliability, observability, cost, security, and rapid 0-to-1 delivery across the stack.
Top Skills:
AlembicAmazon EcrAmazon RdsAmazon S3Anthropic ApiApplication Load BalancerAws EcsAws FargateAws Secrets ManagerClaude Agent SdkCloudflareDockerFastapiGithub ActionsGraphragKnowledge GraphsLanggraphMcpOpentofuPostgresPymupdfPythonPython-DocxReactSQLSqlalchemyTailwind CssTerraformTypescriptVercel Ai SdkVite
Professional Services • Consulting
Build the execution layer of an AI-driven product, including persistent context, autonomous operation, evaluation systems, infrastructure, and user experiences across documents, messaging, and mobile. The role requires end-to-end architectural ownership, product and interface design instincts, familiarity with AI assistants, daily use of AI tooling, and experience creating measurement methods that influence team decisions.
Top Skills:
Ai AgentsAi AssistantsAi Evaluation SystemsAi ToolingAutonomous OperationPersistent Context
Artificial Intelligence • Information Technology • Software
Define and lead Omnissa’s GTM Engineering function, including its roadmap, architecture, tools, and team growth. Build production-grade signal, enrichment, routing, scoring, and agentic workflow systems across Sales, Marketing, Customer Success, and RevOps. Own Clay and its integrations with Salesforce and other GTM platforms, measure business impact, manage vendors, improve data quality, and drive adoption globally.
Top Skills:
APIsClayHubspotJavaScriptJSONLarge Language ModelsMarketoMcpsPythonSalesforceSQL
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
