Software Engineering Manager - Sight & Sense (AI/ML)

Job Description

🚀We’re Hiring: Software Engineering Manager – Sight & Sense (AI/ML)

📍Location: Dearborn, MI (Onsite) Local and Near by Location

Work Authorization: Open to USC, Green Card, and H1B Candidates Onl

yEXP - 15

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Minimum Requiremen

• tsBachelor’s degree in Computer Science, AI/ML, or a related technical fiel
• d.Leadership Experience: 3+ years of experience directly managing software engineering teams, with a proven track record of hiring, mentoring, and retaining talen
• t.Technical Background: 5+ years of experience in software engineering, specifically involving AI/ML, Computer Vision, or complex data-driven product
• s.Systems Knowledge: Deep understanding of the machine learning lifecycle (data engineering, model training, inference, and monitoring
• ).Agile Proficiency: Extensive experience leading teams in Agile environments, managing sprints, and optimizing delivery velocit

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What You’ll Be Able to

• DoEngineering Leadership & Mentorship: Lead, coach, and grow a multi-disciplinary team of software and data engineers. Foster a collaborative environment focused on continuous improvement and professional developme
• nt.Technical Delivery: Own the software development lifecycle for Sight and Sense products. Ensure the team delivers high-quality, maintainable code that meets project milestones and performance benchmarks and results in a reliable and highly scalable produ
• ct.Architecture & Strategy: Provide technical oversight for software and hardware architecture, MLOps pipelines, technology stack selection, and controls integration. Balance the need for rapid innovation with the stability required for 24/7 manufacturing environmen
• ts.Cross-Functional Collaboration: Partner closely with Product Managers and stakeholders to translate manufacturing challenges into technical roadmaps and executable engineering backlo
• gs.Platform Integration: Ensure all vision products are built for seamless integration with centralized data and operating platforms, adhering to a "build once, scale everywhere" philosop
• hy.Operational Excellence: Drive engineering best practices, including automated testing, CI/CD, and robust monitoring for models deployed at the "edge" (on-site factory hardwar
• e).Innovation: Drive innovation by integrating Agentic workflows and AI-assisted engineering tools to maximize team productivity and software qual
• ityDeployment Oversight: Work with site-level stakeholders to ensure technical solutions account for real-world variables such as factory lighting, line speeds, and network constrain

ts.