Computer Vision Research Engineer - Intern

Position Overview

Join the team building computer vision applications for the construction trades.

Contractors spend dozens of hours per project doing takeoffs by hand. We're automating that with models that need to perform reliably on messy, real-world drawings.

As a CV/ML Engineering Intern, you'll work on real production problems alongside our ML team, not a side project. You'll own a scoped piece of work end-to-end, get hands-on mentorship from experienced engineers, and see your models reach customers who use them daily.

Responsibilities

- Build and improve CV models for detection, segmentation, or OCR on construction drawings, with guidance from a mentor on the ML team

- Contribute to our pipelines: data preparation, labeling workflows, training runs, and evaluation

- Run experiments, measure results, and clearly communicate what worked and what didn't

- Dig into real customer data to find failure modes and propose fixes

- Work with fullstack and product engineers to help get your work into production

- Read relevant research and test ideas that could improve our models

Desired Attributes

- Currently pursuing a BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field

- Solid foundation in deep learning and computer vision through coursework, research, or projects

- Hands-on experience training models in PyTorch (or similar), whether in class, research, a prior internship, or personal projects

- Strong Python skills

- Curiosity about messy, real-world data: scanned, handwritten, imperfect

- High ownership: you ask good questions, then run with the answers

- Bonus: experience with document AI, OCR, or vision-language models

Details

- 12-16 weeks, full-time, in person in the SF Bay Area

- Paid internship: $55/hr

- Strong interns may be considered for full-time roles

We value learning speed and follow-through. If you want to build models that customers trust with high-stakes decisions, we'd love to talk.

Comp Philosophy

Cash for stability. Bonus for performance. Equity for ownership. Your package reflects your role, your experience, and what you deliver.