Machine Learning Engineer developing and deploying computer vision models for Invision AI’s 3D digital-twin platform. Optimizing ML systems for real-world edge devices and transportation applications.
Responsibilities
Recommend, develop, evaluate, and deploy ML models across product lines
Build and improve data-labeling, training, and evaluation pipelines
Establish evaluation methods connecting model performance to product and business outcomes
Prototype new product capabilities using appropriate technologies
Optimize models for latency, memory usage, power consumption, and accuracy on edge devices
Diagnose and resolve issues affecting deployed models
Monitor production performance and identify model drift, data-quality problems, and retraining needs
Write maintainable, well-tested code and clear technical documentation
Participate in design reviews, code reviews, and technical planning
Share ML knowledge and collaborate with software, product, and other engineering teams
Requirements
A track record of developing and deploying production computer vision models
Strong Python software development skills
Proficiency with frameworks such as PyTorch, TensorFlow and scikit-learn
Practical knowledge of CNNs and modern computer vision architectures
The ability to adapt open-source models to specific products and use cases
Hands-on work optimizing models for resource-constrained or edge environments
Knowledge of experiment tracking, dataset versioning, and ML observability
Skill in designing evaluation metrics that reflect product and business requirements
An understanding of model monitoring and production troubleshooting
Working knowledge of embedded systems and their constraints
Sound software engineering practices, including automated testing, code review, version control, and continuous integration
Strong written and verbal communication skills
Must be legally entitled to work in Canada
Familiarity with Docker or other container technologies
C++ development skills
GPU programming or performance-optimization knowledge
Knowledge of model compression techniques, including quantization, pruning, and knowledge distillation
Familiarity with edge inference tools such as ONNX Runtime, TensorRT
Familiarity with traditional, non-ML image-processing techniques
A background in sensor fusion or geospatial data
Benefits
The opportunity to work on projects that make the world safer and greener
A culture of very high technical standards where quality engineering is valued over quick hacks
A wide variety of technology and tasks, including web development, distributed and edge computing, ML, real-time processing, and computer vision
Join an international team where your voice is heard and your impact is visible
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