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, utilizing data to validate or dismiss strategies, and transition effective solutions into production.

• Train, refine, and assess classical ML, embedding, and LLM models for tasks such as matching, classification, and extraction.

• Choose the most straightforward approach that satisfies quality and budget constraints.

• Design and implement RAG systems, embedding-based retrieval, and LLM-enhanced features on AWS.

• Oversee the entire ML lifecycle, encompassing experiment tracking, model versioning, deployment, monitoring, and retraining.

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

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

• Resolve issues identified through production monitoring.

• Create customer-facing AI platform functionalities for extensive retail and marketplace data.

⛳️ Requirements

• Over 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 addressing noisy labels.

• Direct experience in developing production applications utilizing LLMs, including RAG, embeddings, and vector databases.

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

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

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

• A tendency to build rapidly for learning and to test ideas swiftly.

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

• An inquisitive nature regarding emerging AI tools.

• Preferred: experience in fine-tuning LLMs and making choices between fine-tuning, prompt engineering, and smaller task-specific models.

• Preferred: experience in reducing inference costs through techniques such as distillation, quantization, caching, or tiered model routing.

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

• Preferred: experience with entity resolution or similarity search on a large scale.

• Preferred: comfort with data pipelines and orchestration tools like Airflow.

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

🏝️ Benefits

• Performance-based discretionary bonuses.

• Variable pay plans available for select positions.

• Opportunity for remote work.

• Reasonable accommodations during the application process.