Senior Machine Learning Engineer

This is a fully remote position, open to applicants in Canada.

📋 Description

• Rapidly transform customer challenges from concept to functional prototype.

• Validate or refute methodologies using data and transition successful strategies into production.

• Train, optimize, and assess models for matching, classification, and extraction tasks.

• Choose suitable methods across traditional ML, embedding models, and LLMs based on quality and cost considerations.

• Design and deploy RAG systems, embedding-based retrieval mechanisms, and LLM-powered functionalities on AWS.

• Oversee the complete ML lifecycle, which includes experiment tracking, model versioning, deployment, monitoring, and retraining.

• Create evaluation systems utilizing labeled golden sets, offline precision and recall metrics, LLM-as-judge, and shadow or A/B testing.

• Continuously monitor production models for drift, output quality, latency, and cost per prediction.

• Address issues identified through production monitoring.

• Develop AI features for product matching, classification, and attribute extraction at scale for customers.

⛳️ Requirements

• 7+ years of experience in ML engineering, applied data science, or AI engineering.

• Proven history of deploying ML or AI systems to production and managing them post-launch.

• Practical experience in training and evaluating models, including feature engineering, experiment design, metric selection, handling class imbalance, and managing noisy labels.

• Direct experience in building production applications with LLMs, encompassing RAG, embeddings, and vector databases.

• Familiarity with MLOps practices and tools such as MLflow, SageMaker Pipelines, Weights & Biases, or equivalent.

• Proficient in AWS for ML workloads, including SageMaker, Bedrock, Lambda, ECS/EKS, and S3.

• Strong proficiency in Python and solid engineering practices, including version control, testing, CI/CD, and code reviews.

• Quick to build and learn, with a tendency to test ideas swiftly.

• Regular use of AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar tools.

• An inquisitive nature about emerging AI tools.

• Preferred: Experience with LLM fine-tuning and the ability to choose between fine-tuning, prompt engineering, and smaller task-specific models.

• Preferred: Expertise in reducing inference costs through distillation, quantization, caching, or tiered model routing.

• Preferred: Experience in retail, e-commerce, or product data.

• Preferred: Experience with entity resolution or similarity search at scale.

• Preferred: Comfort with data pipelines and orchestration tools such as Airflow.

• Ability to meet the specified physical demands, including prolonged sitting and occasional lifting or moving of up to 25 pounds.

🏝️ Benefits

• Performance-based discretionary bonuses and variable pay plans are available for certain positions.

• Options for remote work are provided.

• Reasonable accommodations will be made during the application process.