Senior Machine Learning Operations Engineer

About the role

Mercury's use of machine learning in risk decisioning is growing fast in scope and in stakes. Models increasingly drive real-time decisions about fraud and financial crime, and the Machine Learning Platform (MLP) team exists to build a paved path from a trained model to a reliable production deployment, speeding up iteration, and ensuring granular production observability.

Responsibilities

• Build and operate the real-time inference service that scores models for the risk decision engine, with low latency and high availability as first-class requirements
• Own model deployment infrastructure — registry and versioning, CI/CD with performance, bias, and consistency checks, shadow mode, and staged rollouts
• Build model observability: availability, latency, and error monitoring, plus drift detection as a retraining trigger
• Partner with Risk Data Science to take models from a clean development-to-production handoff through to production operation under MLP ownership
• Implement experimentation capabilities such as champion/challenger and canary routing, and explainability outputs like SHAP attributions
• Feel a strong sense of product ownership and actively seek responsibility — we self-organize on small and medium projects, and we want someone excited to help shape and build a brand-new platform team

Requirements

• 5+ years in machine learning engineering, backend software engineering, MLOps, or a closely related field
• Production ML service experience — deploying, serving, and operating models in low-latency, high-availability contexts
• Strong backend engineering fundamentals in Python, with API frameworks like FastAPI or Flask
• Experience with model deployment and lifecycle tooling: model registries, CI/CD for models, versioning, and staged rollout patterns (shadow, canary, champion/challenger)
• Experience building observability and alerting for production services — latency, errors, and ideally model-specific signals like drift
• Comfort with the data layer ML depends on: SQL, key-value/low-latency stores (Redis, DynamoDB, or equivalent), and streaming pipelines (Kafka, Kinesis, Redpanda, or equivalent)

Qualifications

• Familiarity with a modern data stack (Snowflake, dbt, Dagster, Airflow, or similar) is a nice to have
• Experience operating in a regulated, audit-sensitive, or compliance-adjacent environment is a nice to have
• Exposure to functional languages or willingness to work across a stack that includes Haskell, React, and TypeScript is a nice to have

Skills

• Machine Learning Engineering
• Backend Software Engineering
• MLOps

Benefits

• Base Salary: $166,600 - $208,300 for US employees, CAD 157,400 - 196,800 for Canadian employees
• Equity
• Benefits

Pay

• Base Salary: $166,600 - $208,300 for US employees, CAD 157,400 - 196,800 for Canadian employees

Schedule

• Full-time