Senior Manager, Real-Time Edge Computer Vision Inference

Motorola Solutions; Portland (ME); Hybrid; USD 220,000 - 250,000; Full time

Benefits offered by this job

Incentive Bonus Plans

Medical, Dental, Vision benefits

401K with Company Match

10 Paid Holidays

Generous Paid Time Off

Employee Stock Purchase Plan

Paid Parental & Family Leave

Job summary

Motorola Solutions is seeking a Sr Manager of Computer Vision Inference to lead 2-3 ML teams building real-time video analytics pipelines across cameras and edge devices. This role requires deep expertise in computer vision, deep learning, and high-performance inference to deliver production-ready solutions.

The ideal candidate will drive optimization for throughput and latency, partner with hardware, firmware, and research teams, and mentor engineers while shaping a roadmap for scalable video

Qualifications

• Master's or Ph.D. in Computer Vision, Artificial Intelligence, Computer Science, or a closely related quantitative field.
• 7+ years of engineering leadership experience managing computer vision and deep learning engineering teams delivering edge or embedded software solutions into production.
• Technical Depth in ML and ML Inference: Deep, demonstrable expertise in computer vision, machine learning algorithms, and the end-to-end MLOps lifecycle, including 5+ years of hands-on experience building and optimizing computer vision models or implementing computer vision algorithms.
• Proven expertise in solving customer problems using deep learning and computer vision techniques and in building high-performance inference runtimes.
• Team Management: Demonstrated track record of directly managing organizations of 20-30 engineers, setting clear technical goals, and executing on delivery roadmaps under tight timelines.
• Cross-Functional Leadership: Strong ability to collaborate across firmware, silicon vendor ecosystem, research, and product teams to de-risk platform integrations and architectural trade-offs.
• Customer-Driven Technical Leadership (Pathfinding): Track record of translating highly ambiguous product concepts and complex customer needs into clear, executable technical roadmaps and architectural specifications.
• Startup Mentality & Agility: Experience in a start-up or fast-paced environment demonstrating high ownership, bias for action, comfort with ambiguity, and the ability to drive 0-to-1 product development with limited resources.
• Video Analytics & Camera Systems: Hands-on experience building multi-camera video analytics, real-time streaming frameworks, or embedded vision hardware solutions is a plus.

Responsibilities

• Lead 2-3 ML engineering teams dedicated to developing, optimizing, and deploying computer vision and deep learning solutions across cameras and edge appliances.
• Architect and deliver end-to-end video analytics processing pipelines that operate reliably in real-time under tight resource constraints on edge hardware.
• Partner closely with hardware, platform, embedded firmware, and AI research teams to translate model outputs into seamless, commercial-grade video intelligence solutions.
• Drive continuous optimization to maximize throughput and minimize latency across diverse platforms.
• Define and track core performance benchmarks to maintain superior performance in mission-critical environments.
• Foster an engineering culture of excellence, recruiting, mentoring, and retaining high-performing ML and computer vision engineers.

Skills

Team Leadership

ML Inference

Computer Vision

Edge/Embedded

MLOps

Cross-Functional Collaboration

Customer-Driven Tech Leadership

Startup Mentality

Video Analytics & Camera Systems

Education

Master's or Ph.D. in Computer Vision/AI/CS

Job description

Motorola Solutions is seeking a Sr Manager of Computer Vision Inference to lead 2-3 ML teams building real-time video analytics pipelines across cameras and edge devices. This role requires deep expertise in computer vision, deep learning, and high-performance inference to deliver production-ready solutions.

The ideal candidate will drive optimization for throughput and latency, partner with hardware, firmware, and research teams, and mentor engineers while shaping a roadmap for scalable video