Asks for

The ad says“Solid working knowledge of underlying hardware architectures and embedded accelerators (e.g., Ambarella CVFlow, Qualcomm SNPE, NVIDIA Jetson) and their relationship to software performance constraints.”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“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.”
The ad says“High proficiency in computer vision, machine learning, and multi-modal AI ecosystems, with an established reputation among peers for solving difficult algorithmic or deployment problems and serving as a reliable point of contact for code review leadership and technical guidance.”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“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.”
The ad says“High proficiency in computer vision, machine learning, and multi-modal AI ecosystems, with an established reputation among peers for solving difficult algorithmic or deployment problems and serving as a reliable point of contact for code review leadership and technical guidance.”
The ad says“Solid working knowledge of underlying hardware architectures and embedded accelerators (e.g., Ambarella CVFlow, Qualcomm SNPE, NVIDIA Jetson) and their relationship to software performance constraints.”
The ad says“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.”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“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.”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“Solid working knowledge of underlying hardware architectures and embedded accelerators (e.g., Ambarella CVFlow, Qualcomm SNPE, NVIDIA Jetson) and their relationship to software performance constraints.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).”
The ad says“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.”
The ad says“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.”

Who we are:

Geotab ® is a global leader in IoT and connected transportation and certified “Great Place to Work™.” We are a company of diverse and talented individuals who work together to help businesses grow and succeed, and increase the safety and sustainability of our communities.

Geotab is advancing security, connecting commercial vehicles to the internet and providing web-based analytics to help customers better manage their fleets. Geotab’s open platform and Geotab Marketplace ®, offering hundreds of third-party solution options, allows both small and large businesses to automate operations by integrating vehicle data with their other data assets. Processing billions of data points a day, Geotab leverages data analytics and machine learning to improve productivity, optimize fleets through the reduction of fuel consumption, enhance driver safety and achieve strong compliance to regulatory changes.

Our team is growing and we’re looking for people who follow their passion, think differently and want to make an impact. Ours is a fast paced, ever changing environment. Geotabbers accept that challenge and are willing to take on new tasks and activities - ones that may not always be described in the initial job description. Join us for a fulfilling career with opportunities to innovate, great benefits, and our fun and inclusive work culture. Reach your full potential with Geotab. To see what it’s like to be a Geotabber, check out our blog and follow us @InsideGeotab on Instagram.

Who you are:

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!

What you'll do:

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.

How you'll make an impact:

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.

What you'll bring to the role:

5 to 8 years of relevant industry experience demonstrating varied expertise across a wide array of problems, pressures, and engineering scenarios on a consistent basis.
5+ years of specific hands-on experience in applied machine learning, working with large-scale datasets, and successfully deploying models in resource-constrained environments.
Bachelor’s degree or Master's degree/diploma in Computer Science, Software/Electrical Engineering, Physics, Mathematics, or a related quantitative field (or an equivalent combination of advanced education and exceptional industry experience).
Advanced hands-on proficiency in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow, OpenCV) alongside edge deployment libraries (ONNX Runtime, OpenVINO, CoreML).
Solid working knowledge of underlying hardware architectures and embedded accelerators (e.g., Ambarella CVFlow, Qualcomm SNPE, NVIDIA Jetson) and their relationship to software performance constraints.
High proficiency in computer vision, machine learning, and multi-modal AI ecosystems, with an established reputation among peers for solving difficult algorithmic or deployment problems and serving as a reliable point of contact for code review leadership and technical guidance.
Strong analytical, problem-solving, and communication skills with the ability to make objective decisions, balance tech debt with business delivery, and mentor junior team members.

If you got this far, we hope you're feeling excited about this role! Even if you don't feel you meet every single requirement, we still encourage you to apply.

Please note: Geotab does not accept agency resumes and is not responsible for any fees related to unsolicited resumes. Please do not forward resumes to Geotab employees.

This posting is for an existing vacancy.

Why job seekers choose Geotab:

Flex working arrangements
Home office reimbursement program
Baby bonus & parental leave top up program
Online learning and networking opportunities
Electric vehicle purchase incentive program
Competitive medical and dental benefits
Retirement savings program

*The above are offered to full-time permanent employees only

How we work:

At Geotab, we have adopted a flexible hybrid working model in that we have systems, functions, programs and policies in place to support both in-person and virtual work. However, you are welcomed and encouraged to come into our beautiful, safe, clean offices as often as you like. When working from home, you are required to have a reliable internet connection with at least 50mb DL/10mb UL. Virtual work is supported with cloud-based applications, collaboration tools and asynchronous working. The health and safety of employees are a top priority. We encourage work-life balance and keep the Geotab culture going strong with online social events, chat rooms and gatherings. Join us and help reshape the future of technology!

We believe that ensuring diversity is fundamental to our future growth and progress and is an integral part of our business. We believe that success happens where new ideas can flourish – in an environment that is rich in diversity and a place where people from various backgrounds can work together. Geotab encourages applications from all qualified individuals. We are committed to accommodating people with disabilities during the recruitment and assessment processes and when people are hired. We will ensure the accessibility needs of employees with disabilities are taken into account as part of performance management, career development, training and redeployment processes. If you require accommodation at any stage of the application process or want more information about our diversity and inclusion as well as accommodation policies and practices, please contact us at careers@geotab.com. By submitting a job application to Geotab Inc. or its affiliates and subsidiaries (collectively, “Geotab”), you acknowledge Geotab’s collection, use and disclosure of your personal data in accordance with our Privacy Policy. Click here to read our Privacy Notice.

The annual base salary for this position is the expected annual salary for this role, and may be subject to change. Geotab offers various perks and benefits and other compensation components that an individual may be eligible for. The actual base salary for this position depends on a variety of factors such as but not limited to skills, qualifications, education and overall experience, including the location the applicant lives while performing the job. This also includes equity with other team members and alignment with local market data. All offers of employment are contingent upon proof of eligibility to work and the individual's ability to pass a background check.

Hiring Range

$104,400—$135,700 CAD