Machine Learning Engineer Computer Vision

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

Role Type: New position –
current requirement

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, optimize and add
  advanced MLOps capabilities to existing Vertex AI Kubeflow Pipelines
  (KFP).
  
• Model Optimization & Conversion: Manage the
  complex conversion of models from frameworks like TensorFlow into highly
  optimized TensorFlow Lite (TFLite) artifacts for edge inference (e.g.,
  handling Int8 full integer quantization 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.
  

Requirements

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
  TensorFlow 2.x and/or PyTorch.
  
• Edge ML: Proven experience optimizing deep learning
  models for edge devices using TFLite (e.g., post -training quantization,
  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 containerizing ML workloads using Docker.
  
• Cloud Infrastructure: Solid understanding of Google
  Cloud Storage (GCS) for managing massive datasets and handling model
  artifact 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 TensorFlow/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, parallelized 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.
  

Benefits

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

The final compensation offered
will depend on local market conditions and geographic location, as well as
job -related factors such as the candidate’s knowledge, skills, qualifications,
relevant experience, and education/training. Compensation may also include
additional components such as benefits, and/or other incentives, where
applicable. In accordance with new employment standards requirements, we retain
copies of this job posting and applicant information for three (3) years after
the posting is removed. We do not use AI technology; all applications are also
reviewed by our recruitment team.

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