[Remote] Senior Machine Learning Operations Engineer

Note: The job is a remote job and is open to candidates in USA. Mercury is a fintech company focused on creating exceptional banking experiences for startups. They are seeking a Senior Machine Learning Operations Engineer to build and operate a real-time inference service for their risk decision engine, ensuring low latency and high availability. The role involves owning model deployment infrastructure, building observability, and partnering with data science teams to manage the production ML lifecycle.

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

Skills

• 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)
• 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

Benefits

• The total rewards package at Mercury includes base salary, equity, and benefits.
• Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry.
• New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
• Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer.
• We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs.
• If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.

Company Overview

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