Mercury - Senior Machine Learning Operations Engineer
Requirements
• Familiarity with a modern data stack (Snowflake, dbt, Dagster, Airflow, or similar) • Experience operating in a regulated, audit-sensitive, or compliance-adjacent environment • Exposure to functional languages or willingness to work across a stack that includes Haskell, React, and TypeScript • The total rewards package at Mercury includes base salary, equity, and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers. • Our target new hire base salary ranges for this role are the following: • US employees (any location): $166,600 - $208,300 • Canadian employees (any location): CAD 157,400 - 196,800
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
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