Job Opportunities API

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 rigor and business context, understanding when in-house models create competitive advantage vs. when vendor APIs are sufficient. - Collaborate with software engineers, data engineers, and product stakeholders to integrate ML solutions into CompanyCam's platform. - Communicate clearly with non-technical audiences about feasibility, requirements, and trade-offs of proposed solutions. WHAT YOU'LL BRING MUST-HAVES These are our non-negotiables: - Show up: give us your best and have the courage to do difficult but necessary stuff. - Grow up: be humble, take responsibility, learn continuously, and have a growth mindset. - Do good: treat your co-workers and customers the way you want to be treated. - 3+ years of experience shipping machine learning models to production (not just training them). - Experience with computer vision techniques including image classification, segmentation, and object detection. - Strong coding skills in Python with proficiency in PyTorch or TensorFlow and comfort with modern architectures (transformers, CNNs, etc.). - Strong SQL skills including joins, subqueries, window functions, and CTEs. - Proficiency in data analysis, cleaning, transformation, and feature engineering. - Experience with version control (Git), experiment tracking, and ML development best practices. - Ability to explain technical concepts to non-technical stakeholders through clear writing and presentations. - You live and work permanently in the U.S. (We're not set up to hire outside the U.S.). NICE-TO-HAVES You'll encounter the following with varying frequency. Experience with them is a plus, but not required: - Embeddings, vector databases, and similarity search - On-device model deployment (e.g., Core ML, TensorFlow L