Senior Machine Learning Operations Engineer

🔥 12 hours ago

🌐 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

1001 - 5000 employees

Founded 2017

💳 Fintech

🏦 Banking

☁️ SaaS

Fintech • Banking • SaaS

Mercury is a software-driven fintech that provides business and personal banking products built for startups and small to mid-size companies. Its platform offers business checking and savings, Treasury products, business credit cards and spend management, payments and invoicing, accounting integrations and AI-powered automations, plus APIs and developer tools. Mercury partners with FDIC-insured banks to offer deposit protection, emphasizes virtual cards, granular spend controls, and workflow automations for teams, agencies, ecommerce brands, VC funds, and crypto businesses. The product is positioned as an all-in-one banking and finance platform delivered through a modern app and API.

📋 Description

• Build and operate the real-time inference service that scores models for the risk decision engine, prioritizing low latency and high availability • Own model deployment infrastructure, including registry and versioning, model CI/CD checks, shadow mode, and staged rollouts • Build model observability for availability, latency, errors, and drift detection as a retraining trigger • Partner with Risk Data Science to move models from development to production operation under MLP ownership • Implement experimentation capabilities such as champion/challenger and canary routing • Implement explainability outputs such as SHAP attributions • Take product ownership, self-organize on projects, and help shape a 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 SQL, key-value/low-latency stores such as Redis or DynamoDB, and streaming pipelines such as Kafka, Kinesis, or Redpanda • Familiarity with a modern data stack such as Snowflake, dbt, Dagster, or Airflow • Experience operating in a regulated, audit-sensitive, or compliance-adjacent environment • Exposure to functional languages or willingness to work across Haskell, React, and TypeScript

🏖️ Benefits

• Equity (stock options/RSUs) • Benefits package • Reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs