Computer Vision Engineering

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Senior Computer Vision Engineer who will deliver advanced, high-precision Edge AI technical contributions for Geotab camera systems. Operating with a high degree of execution and independence, this role designs, develops, and maintains scalable Compositional Video Understanding models while actively improving code structure and architecture for long-term maintainability. Recognized by peers for technical guidance on complex failure modes, the Senior Engineer works closely with Technical Leads to contribute to major feature releases, upholds a high technical bar, and actively mentors less senior developers to drive team velocity. If you love technology, and are keen to join an industry leader — we would love to hear from you!

As a Senior Computer Vision Engineer, your key area of responsibility will be delivering advanced, high-precision Edge AI technical contributions for Geotab camera systems. You will design, implement, and validate novel deep learning architectures for real-time edge processing while continuously improving code structure, training frameworks, and deployment pipelines. You will need to work closely with Technical Leads, adjacent engineering teams, camera software engineers, platform developers, immediate team members, product managers, internal partners, and external candidates through the interview process.

To be successful in this role you will be a pragmatic project owner and self-starter with strong analytical skills, able to tackle systemic challenges under tight time constraints, evaluate systemic impacts, and mentor less senior engineers to elevate team-wide capabilities. In addition, the successful candidate will have advanced hands-on proficiency in computer vision, machine learning, and edge deployment ecosystems, with the ability to optimize models for ultra-low-latency performance, troubleshoot complex failure modes, and balance tech debt with business delivery.

• High-Precision Model Development: Design, implement, and validate novel, high-precision CV/ML deep learning architectures (CNNs, Transformers, etc.) for real-time edge processing, covering object detection, segmentation, tracking, scene understanding, and sensor fusion.
• Architecture & Clean Code Structure: Continuously improve codebase structure, model training frameworks, and deployment pipelines in service of testability, robustness, and maintainability.
• Design Documentation: Independently write, co-write, and critically review technical design documentation for complex camera systems and feature sets.
• Edge Optimization & Hardware Alignment: Optimize models intensely for accuracy and ultra-low-latency performance; apply advanced quantization, pruning, and knowledge distillation techniques to ensure reliable deployment on edge systems with hardware accelerators.
• Production Operations & CI/CD: Build, automate, and refine edge model monitoring tools and continuous integration/deployment (CI/CD) pipelines to guarantee sustained reliability and seamless updates in the field.
• System Failure Mode Investigation: Diagnose and troubleshoot complex model training failures, inference bottlenecks, and live field performance issues, drawing on past system failure experiences to lead big-picture investigations.
• Pragmatic Project Ownership: Independently tackle systemic challenges under tight time constraints or stressful situations; evaluate, prioritize, and logically present appropriate solutions to technical leads and stakeholders.
• Team-Enabling Execution: Proactively take ownership of unowned, complex, or undesirable technical tasks that systematically enable the entire development team to move faster.
• Cross-Functional Collaboration: Partner with adjacent engineering teams, camera software engineers, and platform developers to clear roadblocks and execute major feature releases, escalating problems with a wider corporate scope appropriately.
• Individual Coaching: Assist, teach, and mentor less senior engineers and interns on an individual basis, sharing domain expertise to elevate team-wide capabilities.
• Hiring Pipeline Participation: Actively participate in Geotab's engineering interview process by reviewing candidates, conducting technical interviews, submitting evaluations, or attending recruiting events.
• Stakeholder Alignment: Collaborate with immediate team members, product managers, and internal partners to smoothly execute project timelines and manage delivery risks.