Senior Machine Learning Engineer (AI)

Location: Remote, Toronto, ON, Canada

Base annual pay: CAD $160,000–$200,000, plus potential performance-based discretionary bonuses and variable pay plans.

Company

hirify.global provides competitive intelligence products for brands, retailers, and marketplace sellers, including price, market, and MAP intelligence.

What you will do

• Take customer problems from initial ideas through prototypes, data validation, and production deployment.
• Train, fine-tune, and evaluate classical ML, embedding, and LLM models for product matching, classification, and attribute extraction.
• Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS.
• Own experiment tracking, model versioning, deployment, monitoring, retraining, and the broader ML lifecycle.
• Build evaluation systems using labeled golden sets, precision and recall, LLM-as-judge methods where appropriate, shadow testing, and A/B testing.
• Monitor production models for drift, output quality, latency, and cost per prediction, then improve issues found.

Requirements

• 7+ years of experience in ML engineering, applied data science, or AI engineering.
• Proven experience shipping ML or AI systems to production and owning them after launch, including explaining architecture, trade-offs, and success metrics.
• Hands-on experience with feature engineering, experiment design, metric selection, class imbalance, noisy labels, and production model evaluation.
• Hands-on experience building production applications with LLMs, including RAG, embeddings, and vector databases.
• Working knowledge of MLOps practices and tooling such as MLflow, SageMaker Pipelines, Weights & Biases, or similar.
• Experience with AWS ML workloads, including SageMaker, Bedrock, Lambda, ECS/EKS, and S3.
• Strong Python skills and solid engineering practices covering version control, testing, CI/CD, and code review.
• Ability to build and test ideas quickly, preferring rapid validation over extended debate.
• Daily use of AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar, plus curiosity about new AI tools.

Nice to have

• Experience with LLM fine-tuning and choosing between fine-tuning, prompt engineering, and smaller task-specific models.
• Experience reducing high-volume inference costs through distillation, quantization, caching, or tiered model routing.
• Background in retail, e-commerce, or product data, including catalog matching, taxonomy mapping, or attribute extraction.
• Experience with entity resolution or similarity search at scale.
• Comfort with data pipelines and orchestration tools such as Airflow.

Culture & Benefits

• Remote work is available; when not remote, work takes place in a professional office environment.
• Full-time employee position with base pay determined individually based on experience, education, qualifications, geography, and internal equity.
• Performance-based discretionary bonuses and variable pay plans may be available.
• Reasonable accommodations are available during the application process and for essential job functions.
• The employer provides an equal-opportunity work environment free from discrimination, harassment, and retaliation.