Senior Machine Learning Operations Engineer

🕒 June 19

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

🏄 California, New York, +1 more states – Remote

🏄 California – Remote 🗽 New York – Remote 🌲 Oregon – Remote

💵 $166.6k - $208.3k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Mercury

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2019

💳 Fintech

💸 Finance

☁️ SaaS

Fintech • Finance • SaaS

Mercury is a financial technology company that provides online business banking services, although it is not a bank itself. It partners with FDIC-insured banks like Choice Financial Group, Column N. A. , and Evolve Bank & Trust to offer banking services. Mercury aims to simplify financial operations for startups and businesses by offering a range of services including checking and savings accounts, treasury management, corporate cards, expense management, invoicing, and accounting automations. The platform is designed to streamline various banking tasks and enhance financial workflows with features like fraud monitoring and account security. It also offers investment opportunities through its Mercury Treasury product and startup support through its Mercury Raise platform.

📋 Description

• 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)

🏖️ Benefits

• Competitive salary • Equity • Health insurance plans • Paid time off • Remote work options