Applied ML Engineer (Computer Vision)

About Boon

Boon is the professional AI platform built specifically for construction. Founded in the San Francisco Bay Area in 2023 by product and engineering leaders from Samsara, Apple, Google and DoorDash. Boon is backed by leading Silicon Valley venture capitalists.

Our AI agents embed directly into existing workflows, from preconstruction estimating to bid management. They automate the repetitive tasks that drain time and margins while surfacing the insights leaders need to make faster and more confident decisions.

The result is measurable impact. Teams move faster, bids are submitted sooner, win rates increase, and costs are reduced. Boon enables construction companies to build more, generate more revenue, and grow with confidence.

About the Role

As an Applied ML Engineer focused on Computer Vision at Boon, you will build production computer vision systems that transform how construction companies process and understand visual data. This is not a research position-you'll be writing code, deploying models, and owning critical pieces of our ML infrastructure from day one. While you'll receive mentorship from senior engineers, you'll have real ownership over specific components of our vision pipeline and the opportunity to see your work directly impact customers.

Our vision systems tackle diverse challenges across construction : from processing complex engineering plans and construction drawings to understanding technical documents. You'll work on parsing intricate blueprints and architectural diagrams, extracting structured data from multi-page technical specifications, and building intelligent systems that can reason about complex visual information. You'll bring the latest advances in computer vision research into traditional industries that power a significant portion of the global economy.

In this role, you will :

• Build and deploy computer vision models for complex document understanding, including OCR, edge detection, vector detection, and semantic comprehension of construction drawings and technical diagrams
• Own specific components of our ML pipeline-from data annotation tools to model training scripts to inference services-and be responsible for their reliability and performance
• Implement and fine-tune state-of-the-art computer vision models, whether adapting open-source solutions or building custom architectures for our unique use cases
• Develop annotation pipelines, training workflows, and monitoring systems to ensure our models perform well in production
• Work directly with construction customers to understand their needs, rapidly experiment with solutions, and iterate based on real-world feedback
• Collaborate with senior engineers to learn best practices for production ML while contributing meaningfully to our computer vision capabilities

You're a Great Fit If :

Minimum requirements for the role :

Compensation

At Boon, we want to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contribution to the company. With that in mind, we carefully consider a number of factors to determine the appropriate starting pay for an employee, including their primary work location and an assessment of a candidate's skills and experience, as well as market demands and internal parity. This estimate can vary based on the above mentioned factors, so the actual starting annual base salary may be above or below this range. A Boon employee may be eligible for additional forms of compensation, depending on their role, including sales incentives, discretionary bonuses, and / or equity in the company.

Boon Is An Equal Opportunity Employer

As an equal-opportunity employer, Boon is committed to providing employment opportunities to all individuals. All applicants for positions at Boon will be treated without regard to race, color, ethnicity, religion, sex, gender, gender identity and expression, sexual orientation, national origin, disability, age, marital status, veteran status, pregnancy, or any other basis prohibited by applicable law.