🛠 Tech Stack

💼 About This Role

You'll build and deploy applied ML systems that automate high-impact customer operations like disputes, refunds, and fraud detection. You'll design LLM-powered pipelines to extract structured data from unstructured sources, taking models from experimentation to scalable real-world deployment.

🎯 What You'll Do

• Develop and deploy ML models to automate customer operations
• Build and maintain production-grade ML pipelines (training, deployment, monitoring)
• Design and implement LLM-based systems for structured data extraction
• Collaborate with Engineering, Product, and Operations teams to deliver scalable ML solutions

📋 Requirements

• 2+ years of experience as a Machine Learning Engineer
• Strong proficiency in Python and production-quality code
• Experience with gradient-boosted decision trees (e.g., XGBoost, LightGBM, CatBoost)
• Hands-on experience with LLM APIs (OpenAI, Anthropic) including prompt engineering

✨ Nice to Have

• Familiarity with unstructured data processing (document parsing, OCR)
• Experience with ML lifecycle tools like Airflow, MLflow, or Kubeflow
• Ability to debug complex systems and review code in large production codebases

🎁 Benefits & Perks

• 💰 Competitive base salary with equity and performance rewards
• 🏥 Comprehensive health, dental, vision coverage
• 🏡 Flexible remote-first work model across Canada
• 📱 Wellness, technology, lifestyle spending allowances
• 📅 Generous PTO and parental leave

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Phone Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min