Junior Lead ML Engineer - Computer Vision

About this role

About the Role You’ll work alongside experienced engineers to improve our AI-enabled plan ingestion pipeline—from raw PDFs to clean, dependable outputs that power takeoff, design, and downstream automation. You’ll begin by contributing to well-scoped projects and supporting senior engineers, with the opportunity to take on greater ownership as you grow. What You’ll Do Support the development and improvement of machine learning systems for object detection, segmentation, document understanding, and information extraction from building plans Train, evaluate, and debug computer vision models using real-world construction data Help build and maintain datasets, labeling workflows, preprocessing pipelines, and evaluation tools Contribute to experiments involving computer vision, document understanding, and related machine learning techniques Assist with integrating models into production applications and APIs Write clean, testable, and maintainable Python code Investigate model failures and help identify opportunities to improve accuracy and reliability Collaborate with senior engineers to understand technical requirements and turn them into working solutions Document experiments, results, decisions, and lessons learned Learn and apply engineering practices for testing, deployment, observability, and maintainability Who We’re Looking For 1–2 years of professional, internship, research, or equivalent project experience in machine learning, computer vision, or a closely related area A degree in computer science, engineering, mathematics, data science, or a related technical field, or equivalent practical experience Strong Python fundamentals and experience using PyTorch or a similar deep learning framework Familiarity with computer vision tasks such as object detection, image segmentation, classification, or OCR Basic understanding of image processing concepts and tools such as OpenCV Familiarity with Linux and Git Experience working with data, training models, evaluating results, and debugging failures Willingness to ask questions, receive feedback, and learn from more experienced engineers Clear communication skills and comfort working with a remote and cross-cultural team We do not expect junior candidates to have experience with every technology listed in this description. Strong fundamentals, curiosity, and evidence that you can learn quickly matter more than checking every box. What Makes You a Great Fit You have strong technical fundamentals and are excited to apply them to real-world problems You enjoy experimenting, debugging, and understanding why a model succeeds or fails You take responsibility for your work while knowing when to ask for help You care about writing clear, reliable code—not just producing promising model results You are curious, motivated to improve, and comfortable working on problems without obvious solutions You communicate clearly about your progress, questions, and blockers Bonus Points Academic, internship, or personal project experience involving document understanding, OCR, or technical drawings Experience with detection or segmentation frameworks Familiarity with FastAPI, Docker, ClearML, or MLflow Interest in multi-modal models, language models, NLP, or retrieval-augmented generation Exposure to model deployment, inference optimization, or data-labeling workflows A portfolio, GitHub repository, research project, or other examples of technical work What We Offer Competitive salary + meaningful equity Comprehensive benefits (health, dental, vision) Professional development budget (courses, conferences, research exploration) Mentorship from experienced engineers Real-world ML problems with direct impact Clear opportunities for increased ownership and career growth Interview Process Screening call Online skills assessment 30-minute conversation with our CPO Technical interview with our CTO and engineering team 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.