Document AI Research Lead San Francisco, CA 200K-350K

Document AI Research Lead - San Francisco CA - $200K-$350K

Location: San Francisco CA (In-person 5-6 days/week)

Compensation: $200000-$350000/year plus competitive equity

A fast-growing AI startup is building advanced vision-language systems for understanding unstructured documents and is looking for a hands-on ML researcher to help shape the next generation of production AI models.

This is a highly technical research-focused role for someone who enjoys taking ideas from experimentation all the way through deployment. Youll work directly with the founders and play a key role in defining the future of the companys ML capabilities.

What Youll Own

Train and deploy state-of-the-art models for document understanding and parsing unstructured data

Experiment with novel approaches to layout models and vision-language systems

Build data pipelines and evaluation frameworks to improve model performance

Integrate research into production-ready systems

Collaborate closely with engineering to bring models into real-world applications

Influence product direction and technical strategy alongside the founding team

What Youll Bring

Strong experience training and deploying AI models

Deep understanding of computer vision VLMs or related machine learning domains

Hands-on experience building and evaluating production ML systems

Ability to move comfortably between research experimentation and implementation

Strong ownership mentality and willingness to operate in a fast-moving startup environment

Comfortable working in person in San Francisco

Nice to Have

PhD or equivalent research experience in computer vision VLMs or related areas

Publications at top-tier AI conferences

Experience with model serving inference optimization or large-scale deployment

Background at leading AI research labs or quantitative firms

Strong track record translating research into production systems

This is a rare opportunity to join an early-stage team and work directly with founders on challenging problems at the intersection of computer vision and applied AI.