Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

As a Machine Learning Engineer, you will work across the full machine learning lifecycle, from data collection and labeling strategy through training, evaluation, deployment, monitoring, and ongoing improvement. Your focus will be developing and maintaining computer vision models used in real-world products. This is an applied engineering position focused on building, shipping, and maintaining production ML systems. You will work on projects expanding capabilities in object detection, image classification, geospatial tracking, and sensor fusion, with models deployed to resource-constrained edge devices. You will build accurate, efficient, and principled solutions, advancing products, solving customer problems, and shaping ML engineering practices.

Responsibilities

Recommend, develop, evaluate, and deploy ML models across product lines
Build and improve data-labeling, training, and evaluation pipelines
Establish evaluation methods that connect 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
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

Skills

Python
PyTorch
TensorFlow
scikit-learn
CNNs
Computer Vision
Object Detection
Image Classification
Geospatial Tracking
Sensor Fusion
Edge Devices
Experiment Tracking
Dataset Versioning
ML Observability
Model Monitoring
Production Troubleshooting
Embedded Systems
Automated Testing
Code Review
Version Control
Continuous Integration
Docker
C++
GPU Programming
Model Compression
Quantization
Pruning
Knowledge Distillation
ONNX Runtime
TensorRT
Image Processing
Sensor Fusion
Geospatial Data

Location

Toronto

Work Type

Full-time
Hybrid

Experience Level

Production ML Systems
Computer Vision Models
Edge Environments

Salary/Compensations

Competitive salary package including equity

Benefits

RRSP Plan
Health and Dental
4 weeks holiday

About the Company

Invision AI is building a universal AI platform for computer vision applications.
Powered by a unique multi-camera stack that generates high-integrity 3D digital twins of dynamic environments, our technology powers disruptive, market-leading solutions in intelligent infrastructure and global Transportation.
A Mission that Matters: The opportunity to work on projects that make the world safer and greener
Excellence: A culture of very high technical standards where quality engineering is valued over quick hacks
Technical Challenge: A wide variety of technology and tasks, including web development, distributed and edge computing, ML, real-time processing, and computer vision
Growth Environment: Join an international team where your voice is heard and your impact is visible