Senior Machine Learning Operations Engineer

About the role

In this role, you will be part of the Machine Learning Platform team, focusing on building and operating systems that ensure low-latency and high-availability for real-time model inference. Your work will directly impact how models are deployed and monitored, ensuring they meet business needs in fraud risk decisioning.

Success in this position means effectively managing the entire ML lifecycle from deployment to observability. You will collaborate closely with Data Science teams to streamline the transition from model development to production, while also implementing robust monitoring and retraining mechanisms.

The salary range for this role is $166,600 to $208,300 for US employees, reflecting competitive pay in the fintech industry.

Skills that matter here

• Python: You will use Python for backend engineering and API development.
• FastAPI: FastAPI will be utilized for building APIs that support model serving.
• Flask: Flask is part of the tech stack for developing backend services.
• SQL: SQL skills are necessary for managing the data layer that supports ML operations.
• Redis: Redis will be used for low-latency data storage needs.
• Kafka: Kafka is included for building streaming data pipelines essential for real-time inference.

Who this role suits

• You have a strong technical background with at least 5 years in machine learning engineering or related fields.
• You thrive in collaborative environments and enjoy taking ownership of projects.
• You are detail-oriented and have experience in high-stakes, compliance-sensitive environments.
• You are comfortable with self-organizing within a team and are motivated to help shape new initiatives.

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.