Senior Machine Learning Operations Engineer
Mercury, San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States +1
This job was not on the Mercury job board at our last check. Possibly, Mercury closed the job. We will check the job board again.
Overview
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 fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
What you need
- 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)
Nice to have
- 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