# Founding AI Engineer
**San Francisco, California | $200,000 – $300,000 per year**

## About This Opportunity

This isn't just another AI role. You'll be the founding AI engineer at a company building the infrastructure that makes unstructured enterprise data actually usable. If you've ever felt frustrated by how much critical business information lives in PDFs, scanned documents, and messy images—and you want to build the models that finally unlock it—this is your chance to shape everything from day one.

## What You'll Own

You'll take full ownership of the ML research lifecycle—from problem formulation and experimentation through model training, evaluation, optimization, and deployment. That means you're not just handing off work; you're seeing your ideas through to production.

You'll design and improve state-of-the-art Computer Vision and Document AI models that parse and understand unstructured enterprise data. Think layout models, vision-language systems, and everything in between. You'll lead architectural improvements to vision models and VLM-based document understanding systems, pushing beyond fine-tuning into genuine innovation.

You'll build scalable data pipelines and evaluation frameworks that continuously measure and improve performance. You'll transition research prototypes into reliable, production-grade systems—working shoulder-to-shoulder with engineering to make it happen. And you'll establish best practices for experimentation, reproducibility, benchmarking, and iteration that the whole team follows.

You'll work directly with the founders to shape product direction and long-term technical strategy. When appropriate, you'll contribute to research credibility through publications, benchmarks, or open-source contributions.

## What You Bring

**Education & Experience**
- Master's or PhD in Computer Science, Computer Vision, Document AI, or a closely related field from a top-tier institution
- Up to 6 years of experience post-degree, with a strong academic research background and multiple publications in relevant areas
- Proven experience owning end-to-end ML research initiatives
- Experience in a 0-to-1 startup environment with high ambiguity and rapid iteration
- Background at a leading research lab (DeepMind, FAIR, Microsoft Research, OpenAI, Anthropic) strongly preferred

**Technical Depth**
- Deep expertise in Computer Vision, Document AI, multimodal learning, or related domains
- Experience building or significantly improving model architectures—not just applying existing ones
- Hands-on work with smaller foundation models (3B–7B parameter range)
- Strong Python proficiency and familiarity with modern ML frameworks
- Experience training and deploying models for parsing and understanding unstructured data
- Proven ability to build evaluation pipelines and integrate models into production systems

**The Intangibles**
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