Senior Machine Learning Operations Engineer

от 17 358 $

≈ от 1,5 млн ₽

🌍 УдалённоSeniorПолная занятость🌐 Глобал

от 17 358 $≈ от 1,5 млн ₽

Навык востребован (python).

Нажмите на сигнал, чтобы увидеть, на чём он основан

Наша компания

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

О роли

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.

Чем предстоит заниматься

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

Наши требования

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

US employees (any location): $166,600 - $208,300

Canadian employees (any location): CAD 157,400 - 196,800

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)