Machine Learning Engineer

We are hiring a Machine Learning Engineer on behalf of a well-established organization in Toronto to join their team on a 6-month contract with a possibility of further extension.

As a Machine Learning Engineer, you will design, optimize, and deploy deep learning models for large‑scale, real‑time edge inference. You will own the end‑to‑end lifecycle of computer vision models—from training and evaluation to MLOps automation on Google Cloud and edge deployment.

Key Responsibilities:

Computer Vision Development: Design, train, fine-tune, and evaluate state-of-the-art CNN and object detection models.

Pipeline Enhancement: Maintain and optimize advanced MLOps capabilities within Vertex AI Kubeflow Pipelines (KFP).

Model Optimization & Conversion: Convert models (e.g., TensorFlow/PyTorch) into highly optimized TFLite artifacts for edge inference, managing Int8 quantization and hardware acceleration.

Edge Artifact Management: Architect deployment flows to save and version optimized models in GCS for seamless edge-device retrieval.

Automation & Reliability: Implement automated evaluation gates to ensure newly trained models outperform production baselines before deployment.

Required Qualifications & Skills

4+ years in Machine Learning Engineering, with a focus on Computer Vision.

Deep Learning & Frameworks: Strong mathematical understanding of CNNs and object detection. Deep expertise in TensorFlow 2.x and/or PyTorch.

Edge ML & MLOps: Proven experience with TFLite (quantization, pruning) and building custom components in GCP Vertex AI (KFP).

Software Engineering: Advanced Python skills, containerization via Docker, and cloud storage management (GCS).

Core Competencies: Proactive leadership, critical thinking, complex problem-solving, and a commitment to continuous upskilling in AI/ML trends.

Nice to Have Qualifications

Hands-on experience with the Ultralytics YOLOv8 ecosystem (specifically bridging PyTorch weights to TFLite).

Data orchestration experience using Google Cloud Composer (Apache Airflow).

Familiarity with Google Cloud Dataflow (Apache Beam) for large-scale image preprocessing and TFRecords generation.

Experience with CI/CD for ML pipelines.

Knowledge of Generative AI architectures, RAG pipelines, and multi-agent systems.

Work Arrangement & Conditions

Hybrid Model: Office presence is driven strictly by evolving business needs. You will have the autonomy to manage your day-to-day schedule but are expected to exercise independent judgment and be on-site

whenever collaboration, onboarding, or critical project milestones dictate.

In-Office Cadence: Typically 1–3 days per week during initial onboarding, shifting to a baseline of 1 day per week once established.

Note: Specific remote arrangements cannot be guaranteed against future organizational policy changes.

Travel Requirements: Ability to travel to SSC Toronto at minimum once a month, or as required by the business.

Operational Environment: Standard office setting involving focused computer work and virtual meetings (camera-on expected).

Schedule Flexibility: Ability to work occasional overnights and weekends to support system upgrades and critical project deployments.