About this role
A candidate who enjoys tackling complex technical challenges and thrives in a fast-paced environment would find this role suitable. The team at Geotab is focused on advancing security and connectivity for commercial vehicles by leveraging IoT and machine learning to optimize fleet operations. They utilize data analytics and ML to reduce fuel consumption, enhance driver safety, and ensure regulatory compliance across their platform and marketplace. This role is crucial for developing and deploying advanced AI capabilities directly onto edge devices, enabling real-time insights from camera systems.
The Senior Computer Vision Engineer will be responsible for delivering high-precision Edge AI contributions for Geotab's camera systems. This involves designing, developing, and maintaining scalable video understanding models. The individual will focus on optimizing these models for real-time performance on edge hardware while simultaneously improving the overall code structure and deployment pipelines for long-term maintainability. This position exists to ensure the accuracy and reliability of the AI features powering the connected vehicle solutions.
Success in this role requires a blend of deep technical expertise in computer vision and machine learning, strong engineering discipline, and the ability to act as a technical guide for others. The ideal candidate is a pragmatic self-starter who can manage complex system challenges under tight deadlines and mentor team members to raise the overall technical standard.
What you will do
- Design, implement, and validate novel deep learning architectures, such as CNNs and Transformers, for real-time edge processing across various computer vision tasks.
- Continuously improve the code structure, training frameworks, and deployment pipelines to enhance the testability, robustness, and maintainability of the models.
- Optimize models intensely for ultra-low-latency performance by applying techniques like quantization, pruning, and knowledge distillation for edge deployment.
- Develop and maintain automated edge model monitoring tools and continuous integration/deployment (CI/CD) pipelines to ensure field reliability.
- Diagnose and troubleshoot complex issues related to model training failures, inference bottlenecks, and live field performance problems.
- Independently write and review technical design documentation for intricate camera systems and feature sets.
- Mentor less senior engineers to elevate the capabilities of the entire team.
What you need
- Advanced hands-on proficiency in computer vision and machine learning is required.
- Demonstrated experience in developing and validating deep learning architectures for real-time edge processing is necessary.
- Proven ability to optimize models for ultra-low-latency performance, including knowledge of quantization and pruning techniques.
- Strong analytical skills are needed to tackle systemic challenges and evaluate their impact.
- Experience in improving code structure and deployment pipelines for long-term maintainability is essential.
- The candidate must possess the ability to troubleshoot complex failure modes in both training and live field performance.
- Experience working within edge deployment ecosystems is a mandatory skill.
Nice to have
- Experience with sensor fusion techniques
Benefits
- great benefits
- fun and inclusive work culture
Skills and tech stack
Questions candidates ask
What is the remote policy?
The posting does not explicitly detail the remote work policy.
What is the salary range for this role?
The salary offered for this position is between $104,400 and $135,700 CAD.
What is the primary tech stack used for model development?
The role involves designing architectures using CNNs and Transformers, and optimizing for edge deployment using techniques like quantization and pruning.
How does the team structure support mentorship?
The Senior Engineer is expected to actively mentor less senior developers to drive team velocity and elevate team-wide capabilities.