# Senior Machine Learning Operations Engineer

**Company:** [Mercury](https://hotfix.jobs/companies/mercury)
**Location:** San Francisco, CA, New York, NY, Portland, OR
**Role:** ML Engineering
**Salary:** $167k – $208k/yr
**Experience:** 5+ years
**Skills:** Python, FastAPI, Flask, SQL, Redis, DynamoDB, Kafka, Kinesis, Redpanda, Model Registries, CI/CD, Observability, Drift Detection, Shap
**Posted:** 2026-06-18

> Build and operate Mercury's real-time ML inference platform for fraud risk decisioning. Own model deployment, observability, and lifecycle tooling with strong backend Python fundamentals.

## Job Description

## 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 — self-organize on small and medium projects, and help shape and build a brand-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 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

## Similar roles

- [Senior Applied Research Engineer](https://hotfix.jobs/jobs/9273299c-3014-4320-98e1-ae4b9d661aad) - Drata - San Francisco, CA - $167k – $226k/yr
- [Senior AI Engineer, Agent Harness](https://hotfix.jobs/jobs/c5e7d0e4-19a7-4938-aa4c-9ef179acdd19) - Drata - San Francisco, CA - $167k – $226k/yr
- [Senior Software Engineer, Model Serving](https://hotfix.jobs/jobs/b9f85503-574d-49d4-aa09-a2560df24787) - Databricks - San Francisco, CA - $166k – $225k/yr
- [Senior Machine Learning Engineer - GenAI Platform](https://hotfix.jobs/jobs/56b930a9-1d7c-427a-a3df-a38a7a5021f6) - Databricks - San Francisco, CA - $166k – $225k/yr
- [Senior Software Engineer, AI Infrastructure](https://hotfix.jobs/jobs/dd1c3420-6c0a-47ed-a1a7-de401bdf506c) - Ambient.ai - Redwood City, CA - $168k – $205k/yr

**Apply:** https://hotfix.jobs/jobs/d3b3a89a-90b9-4f8c-995d-168d4de2ff75
**Canonical:** https://hotfix.jobs/jobs/d3b3a89a-90b9-4f8c-995d-168d4de2ff75