Greenhouse ·
full-timeSenior Machine Learning Operations Engineer
Mercury · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States
Mercury is a fintech company that provides banking‑style services to startups, partnering with Choice Financial Group and Column N.A. to offer accounts, cards and payments while emphasizing safety and compliance. The Machine Learning Operations Engineer will join the Machine Learning Platform team, which builds the infrastructure that moves data science models from registry to production and keeps them running reliably. Day‑to‑day work includes designing and operating a low‑latency, high‑availability real‑time inference service that scores models for the fraud‑risk decision engine, owning the model deployment pipeline (registry, versioning, CI/CD with bias and consistency checks, shadow mode and staged rollouts), and constructing observability stacks for latency, errors and data‑drift detection that can trigger retraining. The role also involves collaborating with Risk Data Science to ensure smooth handoffs, implementing experimentation patterns such as champion/challenger and canary routing, and exposing explainability outputs like SHAP values. Ideal candidates bring five or more years of ML engineering, backend software or MLOps experience, strong Python skills with frameworks like FastAPI or Flask, hands‑on familiarity with model registries, CI/CD for models, streaming platforms (Kafka/Kinesis/Redpanda), SQL and low‑latency stores, and a track record of building production observability and alerting. Familiarity with a modern data stack or regulated environments is a plus.
Mercury's use of machine learning in risk decisioning is growing fast in scope and in stakes. Models increasingly drive real-time decisions about fraud and financial crime, and the Machine Learning Platform (MLP) team exists to build a paved path from a trained model to a reliable production deployment, speeding up iteration, and ensuring granular production observability. MLP owns the production ML lifecycle: the systems that take a model from registry through deployment, real-time inference, observability, and retraining. Our Data Science colleagues author and train the models. We build the platform that lets them register, deploy, and observe those models in production without carrying the operational burden themselves. We also serve low-latency, highly available scores to the decision engine that depends on them. The platform supports business decisioning broadly, with our first use cases focused on fraud risk outcomes. At Mercury, we are committed to crafting an exceptional banking* experience for startups. Our team is passionately focused on ensuring our products create a safe environment that meets the needs of our customers, administrators, and regulators. * Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC. 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 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. #LI-GC1 Total Rewards The total rewards package at Mercury includes base salary, equity (stock options/RSUs), 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 USD Canadian employees (any location): $157,400 — $196,800 CAD
| Role | Senior Machine Learning Operations Engineer |
|---|---|
| Company | Mercury |
| Location | San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States |
| Type | internship |
| Compensation | Not disclosed |
| Posted | 2026-09-30 |
| Deadline | Rolling |
Typical process for this type of role
A general guide — the exact steps for this specific listing may vary; check the original posting for details.
- 1ApplicationSubmit your resume through the apply link.
- 2ScreeningRecruiter reviews your background against the role.
- 3AssessmentA technical test, assignment, or coding round, depending on the role.
- 4Interview(s)One or more rounds with the hiring team.
- 5OfferOffer letter with compensation and start date.
Before you apply
0/4Senior Machine Learning Operations Engineer at Mercury: frequently asked questions
- What is the salary for this role?
- Mercury has not stated compensation in the listing. Check the original posting or ask during the application process.
- Is the Senior Machine Learning Operations Engineer position remote, hybrid or onsite?
- The listing marks this role as remote, with San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States as the location.
- What is the application deadline?
- Mercury has not listed a fixed deadline, so apply early in case the opening is filled.
- How do I apply for the Senior Machine Learning Operations Engineer role?
- Use the Apply button on this page. It opens the original listing on job-boards.greenhouse.io, where you submit your application with the company.
More at Mercury
Other jobs at Mercury
- Senior Data Analyst - Reconciliation · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States
- Customer Support Manager - Risk · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States
- Engineering Manager - Bank Accounts · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States
- Senior Analytics Engineer · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States
- Senior Engineering Manager - Mobile · San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States
Explore Related Placements
// similar opportunities
You might also like
Senior Machine Learning Engineer
Andurilindustries
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the e...
Senior Machine Learning Engineer
Speechmatics
We are hiring a Senior Machine Learning Engineer to contribute to cutting-edge Research & Development. As innovators in speech technology, our mission is to Und...
Senior Machine Learning Engineer, Simulation
Waymo
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project...
Senior Engineering Manager - Mobile
Mercury
Our forebears found wisdom in the rhythm of tides and the migration patterns of birds, reading nature's encyclopedia with practiced eyes, while today's explorer...
Senior Engineering Manager - Machine Learning
Super
We are on a mission to pioneer the world’s next era of play. As we grow across Europe and Latin America, we’re building The Playstack - the technology powering ...
Senior Machine Learning Research Engineer
Relationrx
About Relation Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand h...