Senior Machine Learning Operations Engineer

About the role

This role focuses on the production machine learning lifecycle, specifically building and operating real-time inference services for risk decisioning. The successful candidate will be responsible for ensuring models are deployed efficiently, perform with low latency, and remain highly available. This includes owning the infrastructure for model deployment, versioning, and continuous integration/continuous delivery (CI/CD) with checks for performance and bias.

The Machine Learning Platform (MLP) team aims to streamline the path from a trained model to a reliable production deployment, enhancing iteration speed and providing detailed production observability. This involves building systems for model registration, deployment, real-time inference, and retraining. The team supports data scientists by managing the operational aspects of their models in production, delivering scores to the decision engine.

Success in this position means developing robust model observability, including monitoring availability, latency, and errors, alongside drift detection to trigger retraining. The role also requires partnering with Risk Data Science to ensure a smooth transition of models from development to production. The ideal candidate will have a strong sense of product ownership and be eager to contribute to shaping a new platform team.

The listed salary range for US employees is $166,600 - $208,300, which includes base salary, equity, and benefits, and is competitive within the SaaS and fintech industry.

Skills that matter here

• Python: This role requires strong backend engineering fundamentals in Python, utilizing frameworks like FastAPI or Flask for building services.
• FastAPI: Experience with API frameworks such as FastAPI is essential for developing the real-time inference services.
• Flask: Familiarity with Flask is valuable for backend development, complementing FastAPI experience in building robust APIs.
• SQL: Comfort with SQL is necessary for interacting with the data layer that machine learning models depend on.
• Redis: Experience with key-value stores like Redis is required for managing low-latency data access in production ML services.
• Kafka: Familiarity with streaming pipelines such as Kafka is important for handling data streams that feed into or are generated by ML models.

Who this role suits

• Someone with at least five years of experience in machine learning engineering, backend software engineering, or MLOps.
• A candidate who has hands-on experience deploying, serving, and operating machine learning models in production environments with strict latency and availability requirements.
• An individual comfortable with model deployment and lifecycle tooling, including registries, CI/CD for models, versioning, and staged rollout patterns.
• A person who can build observability and alerting for production services, including model-specific signals like drift detection.

From the employer

As part of this role, you will:

• 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.

The ideal candidate for the role has:

• 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.

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. Our target new hire base salary ranges for this role are the following:

• US employees (any location): $166,600 - $208,300
• Canadian employees (any location): CAD 157,400 - 196,800

Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. 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.