Machine Learning Engineer – Computer Vision

Job Description

About the Job: We are seeking a seasoned Machine Learning Engineer – Computer Vision to design, optimise, and deploy deep learning models for large-scale, real-time edge inference. In this role, you will work on the end-to-end lifecycle of computer vision models—from training and evaluation to optimisation, automated governance, and edge deployment—while advancing MLOps capabilities on Google Cloud. You will work at the intersection of deep learning, cloud infrastructure, and edge AI, building reliable, high-performance solutions that scale across devices and continuously improve through automation and data-driven evaluation.

Office

Location:

Toronto

Employment Type: Permanent

Work Arrangement: Hybrid (2 days in office per week)

Position Responsibilities:

• Computer Vision Development:
  Design, train, evaluate, and fine-tune state-of-the-art deep learning models for image classification and object detection tasks.
• Pipeline Enhancement:
  Maintain, optimise and add advanced MLOps capabilities to existing Vertex AI Kubeflow Pipelines (KFP).
• Model Optimization & Conversion:
  Manage the complex conversion of models from frameworks like Tensor Flow into highly optimised Tensor Flow Lite (TFLite) artifacts for edge inference (e.g., handling Int8 full integer quantisation and hardware-specific acceleration).
• Edge Artifact Management:
  Architect the deployment flow to save optimized edge models to Google Cloud Storage (GCS) and manage model versioning for seamless edge-device retrieval, bypassing traditional Vertex AI Endpoints.
• Automation & Reliability:
  Implement automated evaluation gates to ensure newly trained models outperform existing production models before edge deployment.

Required Qualifications:

• Experience:
  
  3‑6 years in Machine Learning Engineering, preferably Computer Vision.
• Deep Learning Foundation:
  Strong mathematical and architectural understanding of deep learning concepts, specifically Convolutional Neural Networks (CNNs) and standard object detection architectures.
• Framework Mastery:
  Deep, hands‑on expertise with Tensor Flow 2.x and/or PyTorch.
• Edge ML:
  Proven experience optimising deep learning models for edge devices using TFLite (e.g., post‑training quantisation, pruning, handling custom ops).
• GCP MLOps:
  Strong proficiency in Google Cloud Platform, specifically building and running custom components in Vertex AI Pipelines (KFP).
• Programming:
  Advanced programming skills in Python, with experience containerising ML workloads using Docker.
• Cloud Infrastructure:
  Solid understanding of Google Cloud Storage (GCS) for managing massive datasets and handling model artefact hand‑offs.
• Critical thinking, effective communication skills – verbal and written, problem solving, and dealing with complexity.

Preferred Qualifications:

• YOLO Expertise:
  Hands‑on experience with the Ultralytics YOLOv8 ecosystem, specifically bridging PyTorch YOLO weights to Tensor Flow/TFLite edge deployments.
• Data Orchestration:
  Experience using Google Cloud Composer (Apache Airflow) to schedule and trigger complex ML training pipelines based on data arrival or model drift.
• Scalable Data Processing:
  Familiarity with Google Cloud Dataflow (Apache Beam) for large-scale, parallelised image preprocessing, augmentation, and dataset formatting (e.g., generating TFRecords).
• CI/CD for ML:
  Experience with continuous integration and continuous deployment practices specifically tailored for machine learning models.
• Generative AI:
  Knowledge or experience in Generative AI architectures, with experience building Retrieval‑Augmented Generation (RAG) pipelines and developing multi‑agent systems.

Salary Range: CAD $100,000–$110,000/year

Infoya is an equal opportunity employer committed to diversity and inclusion. We welcome applications from all qualified individuals, regardless of race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, aboriginal status, or any other legally protected factors.