Senior Computer Vision / ML Engineer

Mad Devs; United States; Remoto; USD 140.000 - 190.000; Tempo pieno

Descrizione del lavoro

Mad Devs seeks a Senior Computer Vision / ML Engineer to develop ML solutions for architectural and technical documents. You will build production-ready pipelines that handle raster images and structured engineering data, ensuring reproducibility.

As a technical lead in a small team, you will communicate with stakeholders to explain model limitations and advocate for better approaches, delivering solutions that meet client needs in real-world deployment scenarios.

Competenze

• Extensive experience deploying ML models to production.
• Strong background in computer vision and ML for real-world data.
• Ability to communicate model limitations and trade-offs to stakeholders.

Mansioni

• Develop ML solutions for architectural drawings and technical documents.
• Build production-ready pipelines integrating raster images and structured data.
• Collaborate with stakeholders to align model capabilities with client needs.

Conoscenze

Computer vision

Machine Learning

Production ML

Leadership

Stakeholder communication

Descrizione del lavoro

Mad Devs seeks a Senior Computer Vision / ML Engineer to develop ML solutions for architectural and technical documents. Candidates with extensive experience in computer vision and machine learning in production should apply.

In this role, you will focus on creating machine learning solutions specifically designed for analyzing architectural drawings and technical documents. Your work will involve building pipelines that integrate various data types, including raster images and structured engineering data, ensuring that the models you develop are production-ready and reproducible.

As a technical leader within a small engineering team, you will communicate directly with stakeholders to explain model limitations and advocate for better approaches based on technical data. Success in this position means delivering effective solutions that meet client needs while managing the complexities of model evaluation and deployment in real-world scenarios.