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Senior Machine Learning Operations Engineer

Mercury· Engineering
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Hybrid (San Francisco, CA / New York, NY / Portland, OR / Remote (Canada or US))$166.6k - $208.3kfull-timePosted 1mo ago

About the role

Mercury is building a real-time, low-latency ML production platform (MLP) to deploy, observe, and retrain models for risk decisioning and fraud prevention. This role focuses on building and operating the real-time inference service, deploying models, observability, and partnering with Risk Data Science to bring models from development to production under MLP ownership.

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

Qualifications

  • 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)

Skills

Required:ML deployment and lifecycle toolingReal-time inference serviceObservability and monitoringPython backend development
Last updated Sep 16, 2026. Interested? Apply on the company site.
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