Senior Machine Learning Operations Engineer

Mercury

Senior Machine Learning Operations Engineer building real-time inference systems for risk decisioning at Mercury. Leading deployment infrastructures and ensuring high availability for ML models in production.

Posted 6/19/2026full-timeRemote • California, New York, Oregon • 🇺🇸 United StatesSenior💰 $166,600 - $208,300 per yearWebsite

Tech Stack

Tools & technologies

DynamoDBFlaskKafkaPythonRedisSQL

About the role

Key responsibilities & impact

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

What you’ll 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)

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning engineeringbackend software engineeringMLOpsPythonAPI frameworksmodel deploymentCI/CDobservabilitySQLstreaming pipelines

Soft Skills

product ownershipresponsibilityself-organization