Lead ML Engineer - Computer Vision
Role summary
Benchmark Construction Technology is seeking a Lead ML Engineer with a deep focus in Computer Vision to own and expand their AI-enabled plan ingestion pipeline. This role involves architecting, building, and deploying ML systems for object detection, segmentation, document understanding, and information extraction from complex building plans. The engineer will also drive R&D in computer vision and NLP, develop multi-modal approaches, and mentor junior engineers. Qualifications include 8+ years of ML Engineering experience, expert Python skills with PyTorch and FastAPI, and proven experience with segmentation/detection frameworks and MLops infrastructure. The role requires strong problem-solving abilities and comfort operating independently in a fast-paced, remote environment.
About Benchmark Construction Technology
Benchmark (formerly BotBuilt, Y Combinator W21) is building the next generation of technology behind American homebuilding. We’re starting with one of the hardest parts of the stack: reliably turning messy, real-world construction plans into structured, usable data.
About The Role
You’ll own and expand our AI-enabled plan ingestion pipeline—from raw PDFs to clean, dependable outputs that power takeoff, design, and downstream automation.
What You'll Do
- Architect, build, and deploy ML systems for object detection, segmentation, document understanding, and information extraction from complex building plans
- Drive R&D across computer vision and/or NLP as applied to construction documents and related data
- Develop multi-modal approaches that combine vision with language features for document analysis (LLMs/NLP). Familiarity with different RAG methods is a plus
- Mentor junior engineers and help raise the bar on execution, code quality, and decision-making
- Ensure systems are reliable, maintainable, testable, and observable in production
Qualifications
- 8+ years of experience in Machine Learning Engineering, with deep focus in computer vision
- Expert Python skills, with strong experience in PyTorch and FastAPI
- Proven work with segmentation models, detection frameworks, and OCR/document pipelines
- Strong image processing fundamentals (OpenCV, geometric transforms, preprocessing, etc.)
- Comfortable in Linux; hands-on with Docker/containerization for deployment
- Experience building and maintaining MLops infrastructure (e.g., ClearML, MLflow)
- Ability to design scalable, high-performance ML systems (not just prototypes)
- Strong problem-solving ability and comfort operating independently in a fast-paced environment
What Makes You a Great Fit
- High agency: you ship, iterate, and fix what’s broken—without waiting for permission
- You’ve delivered ML systems end-to-end in production (data → model → deployment → iteration)
- You’re a strong engineer who cares about getting better—trajectory matters here
- You communicate clearly, especially with a remote and cross-cultural team
- You’re gritty and genuinely enjoy solving hard problems
**Bonus Points**
- Experience building systems for document reading / understanding
- Ambitious, driven, and opinionated about quality and craft
- Experience with deep learning inference frameworks (e.g., TensorRT, PyTorch compile) and optimizing inference performance
- You keep up with current ML/AI developments and can apply them pragmatically
What We Offer
- Competitive salary + meaningful equity
- Comprehensive benefits (health, dental, vision)
- Professional development budget (courses, conferences, research exploration)
- Real-world ML problems with direct impact
- High growth potential
