Join the Risk & Fraud team at Q2 as a Machine Learning Engineer, where you will build and maintain production systems that power fraud prevention products for financial institutions. You’ll work closely with data scientists to deploy machine learning models into scalable environments, ensuring they are reliable and performant. Your day-to-day involves writing clean code, supporting monitoring of ML systems, and collaborating on the continuous improvement of tooling and infrastructure. Essential skills include proficiency in Python, experience with frameworks like PyTorch or TensorFlow, and a strong background in production engineering practices. Ideal candidates have hands-on experience building end-to-end ML solutions and are passionate about delivering high-quality technical products that solve real-world problems for customers.
Build and maintain systems for training, evaluation, and inference of machine learning models.
Deploy machine learning models into production environments to ensure reliable operation at scale.
Write clean, maintainable code following best practices in production engineering.
Monitor and troubleshoot ML systems, including data pipelines and model performance issues.
Collaborate with teams to integrate machine learning models into scalable applications.
What we're looking for
At least 2 years of experience in machine learning and software engineering.
Proficiency in Python and familiarity with ML frameworks like PyTorch or TensorFlow.
Experience writing clean, maintainable code and using version control systems (e.g., Git).
Ability to build and deploy machine learning models into production environments.
Knowledge of cloud platforms such as AWS, GCP, or Azure is preferred.
Familiarity with MLOps concepts including CI/CD pipelines and model monitoring.
Experience in building end-to-end ML systems, including data pipelines and APIs.
Market check
Salary context
This listing doesn't show a salary. Similar roles on FindRole typically pay
$160,500–$240,700.
Peer median band
$160,500–$240,700
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$167,180–$246,150
Middle half of comparable postings.
Based on 239 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Q2
Q2 Holdings is a cloud-based banking software company providing digital banking solutions to banks, credit unions, and alternative financial companies, including consumer and business banking platforms. Industry: Financial Technology & Digital Banking
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PyTorch
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