Startups · AI

Senior Machine Learning Operations Engineer

Mercury · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States · Remote

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About Mercury

Everything you do with money. All in one place. Apply in 10 minutes for business banking that transforms how you operate. Backed by Sequoia, a16z and 500 Global.

About the role

Our target new hire base salary ranges for this role are the following :

What they're looking for

  • 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)
More about this 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.

MLP owns the production ML lifecycle: the systems that take a model from registry through deployment, real-time inference, observability, and retraining. Our Data Science colleagues author and train the models. We build the platform that lets them register, deploy, and observe those models in production without carrying the operational burden themselves. We also serve low-latency, highly available scores to the decision engine that depends on them. The platform supports business decisioning broadly, with our first use cases focused on fraud risk outcomes.

At Mercury, we are committed to crafting an exceptional banking* experience for startups. Our team is passionately focused on ensuring our products create a safe environment that meets the needs of our customers, administrators, and regulators.

* Mercury is a...

Read the full posting on Mercury's site ↗

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