Senior Machine Learning Engineer (AI)

Location: Remote, Ontario, CA, United States

Salary: CAD 160,000–200,000 per year, 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

• Turn customer problems into ML prototypes, validate approaches with data, and move successful solutions into production.
• 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 the ML lifecycle, including experiment tracking, model versioning, deployment, monitoring, and retraining.
• Build evaluation systems using labeled golden sets, precision and recall, LLM-as-judge methods, shadow testing, and A/B testing.
• Monitor and improve production models for drift, output quality, latency, and cost per prediction.

Requirements

• 7+ years of experience in ML engineering, applied data science, or AI engineering.
• Track record of shipping ML or AI systems to production and owning them after launch, including explaining architecture, tradeoffs, and success metrics.
• Hands-on experience with feature engineering, experiment design, metric selection, class imbalance, noisy labels, and production-readiness decisions.
• Hands-on experience building production LLM applications with RAG, embeddings, and vector databases.
• Working knowledge of MLOps practices and tools such as MLflow, SageMaker Pipelines, Weights & Biases, or similar.
• Experience with AWS ML workloads, including SageMaker, Bedrock, Lambda, ECS/EKS, and S3.
• Strong Python and engineering practices including version control, testing, CI/CD, and code review.
• Ability to build and test ideas quickly, with daily use of AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar.

Nice to have

• LLM fine-tuning experience and judgment on when to fine-tune, prompt engineer, or train a smaller task-specific model.
• 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.
• Experience 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 employment with base pay determined individually based on experience, education, qualifications, and geographical differentials.
• Potential performance-based discretionary bonuses and variable pay plans are available for some positions.
• Reasonable accommodations are available during the application process and for employees with disabilities.
• Equal opportunity employment with a workplace free from discrimination, harassment, and retaliation.

Hiring process

• Contact HR by email or phone if accommodation or an alternative application method is required.