Senior Machine Learning Operations Engineer

Role Overview:

• Mercury's ML platform team builds the paved path from model training to production deployment, ensuring reliability and observability.
• This role focuses on the production ML lifecycle including deployment, real-time inference, and retraining.

Key Responsibilities:

• Build and operate the real-time inference service for risk decision engine with low latency and high availability.
• Own model deployment infrastructure including CI/CD, shadow mode, and staged rollouts.
• Build model observability and partner with Risk Data Science for production operation.

Requirements:

• 5+ years in ML engineering, backend engineering, or MLOps with production ML service experience.
• Strong Python and API framework skills, plus experience with model lifecycle tooling and observability.
• Familiarity with data layer technologies like SQL, key-value stores, and streaming pipelines.

Compensation:

• Base salary range for US employees: $166,600 - $208,300; for Canadian employees: CAD 157,400 - 196,800.
• Total rewards include base salary, equity, and benefits.