Machine Learning Engineer - Computer Vision - CompanyCam

Hi, we’re CompanyCam .

We’re a simple-to-use photo documentation and productivity app for contractors of all commercial and home services industries. Packed with intuitive functionality, CompanyCam facilitates unparalleled communication and accountability across a contractor’s entire business. We’re committed to providing a consumer-grade, game-changing experience that helps our users build trust within their company and with their customers.

But don’t let that corporate description fool you—the people behind our buttoned-up product are laid-back (but hardworking), genuine, and kickass, and you could be one of them!

The Role

We're looking for a Machine Learning Engineer with deep computer vision experience to join our ML team.

Contractors capture millions of jobsite photos through CompanyCam every day. As an ML Engineer, you'll turn that visual data into structured understanding — building and shipping computer vision systems that power image classification, document detection, segmentation, multimodal embeddings, and more across 70,000+ projects daily.

This is a small team with outsized reach. You'll own problems end-to-end, from data preprocessing and model training through evaluation and production deployment. You won't be tuning hyperparameters on someone else's model — you'll make architectural decisions and see your work in the hands of real contractors on real jobsites.

Current and near-term problems include segmentation, on-device model deployment, vision-language model integration, and building the evaluation infrastructure to do it all sustainably.

Working At CompanyCam

Our engineering team is remote-first, spanning every time zone in the United States. We welcome people from all backgrounds and really don't care whether or not you have a CS degree or even a high school diploma. All that matters is that you're not a jerk and you're good at what you do.

At CompanyCam we're driven to produce work with meaningful outcomes. That means not just dumping features and "improvements" but being able to reflect and learn from our outputs.

What You'll Do

• Design, train, and deploy computer vision models to production with well-understood performance, latency, and cost characteristics.
• Own the full ML pipeline: data preprocessing, feature engineering, model selection, training, evaluation, and deployment into sustainable inference services.
• Conduct discovery spikes to validate feasibility and inform go/no-go decisions before committing to full development.
• Integrate ML solutions with observability tooling, establishing and maintaining benchmarks to measure improvement and compare approaches.
• Build automated, self-sustaining ML pipelines. Models should train, evaluate, and deploy with minimal manual intervention.
• Inform build-vs-buy decisions with both technical rigo