Job Title: Machine Learning Engineer (Computer Vision)
Location: Toronto (Hybrid – 1 day/week after onboarding)
Employment Type: Contract 6 Months
Pay Rate: $75/h on Inc
Role Overview
We are seeking an experienced Machine Learning Engineer to design, optimize, and deploy computer vision models for large-scale, real-time edge inference. This role will own the end-to-end ML lifecycle, including model development, MLOps automation, cloud deployment, and edge optimization.
Key Responsibilities
- Design, train, fine-tune, and evaluate computer vision and object detection models
- Develop and optimize MLOps pipelines using Vertex AI and Kubeflow Pipelines (KFP)
- Convert and optimize models for edge deployment using TensorFlow Lite (TFLite), including quantization and hardware acceleration
- Build automated validation and deployment workflows to ensure model quality
- Manage model versioning and deployment artifacts in Google Cloud Storage (GCS)
- Collaborate with engineering teams to deliver scalable AI solutions
Required Skills
- 4+ years of experience in Machine Learning Engineering
- Strong experience with Computer Vision, CNNs, and Object Detection
- Deep expertise in TensorFlow and/or PyTorch
- Hands-on experience with Vertex AI, Kubeflow Pipelines (KFP), and GCP
- Experience optimizing models using TFLite
- Strong Python programming skills
- Experience with Docker and cloud-native deployments
- Strong problem-solving and software engineering fundamentals
Nice to Have
- Experience with YOLOv8 (Ultralytics)
- Google Cloud Composer (Airflow)
- Dataflow / Apache Beam
- CI/CD for ML pipelines
- Generative AI, RAG, or Multi-Agent systems
What We're Looking For
- Strong hands-on ML engineer with production deployment experience
- Expertise in building scalable AI solutions on GCP
- Experience deploying models to edge devices
- Ability to work independently in a fast-paced environment
Work Arrangement
- Hybrid model
- Initial onboarding: 1–3 days/week onsite
- Long-term expectation: approximately 1 day/week onsite
- Ability to travel to Toronto office monthly if required
- Occasional after-hours support for deployments and upgrades
