Machine Learning Engineer

q2ebanking; Cary (NC); On-site; USD 110,000 - 160,000; Full time

Job summary

Q2 is seeking a Machine Learning Engineer to join the Risk & Fraud team in the United States. You will build and operate production systems behind fraud detection at scale, protecting trillions in transactions annually and turning models into reliable, real-time services.

You will collaborate with data scientists and engineers, advancing model development from training to deployment and monitoring, with emphasis on performance, latency, and reliability.

Qualifications

• Bachelor's degree in related field and 2+ years of relevant experience.
• Proven experience in ML model development and deployment.
• Strong knowledge of statistics, optimization, probability theory, and experimental methodologies.
• Proficiency in programming languages such as Python, R, or Java.
• Experience with ML frameworks/libraries (TensorFlow, PyTorch, scikit-learn).
• Familiarity with cloud platforms and scalable computing resources.
• Strong analytical, problem-solving, and collaboration skills.

Responsibilities

• Research emerging fraud and abuse patterns and translate that research into new detection approaches.
• Help build next-generation ML products across identity, behavior, and transaction fraud, partnering directly with customers to understand their needs and shape product direction.
• Build and optimize real-time , low-latency ML infrastructure, continually improving its reliability, scalability, and performance.
• Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models, collaborating with data scientists to productionalize models into scalable applications.
• Write clean, maintainable, and well-tested code, following production engineering best practices and leveraging the latest AI tooling.
• Support monitoring and troubleshooting of production ML systems, including data pipelines and model performance.

Skills

Python

R

Java

Statistics

ML concepts

Education

Bachelor's degree in related field

Tools

TensorFlow

PyTorch

scikit-learn

Job description

As passionate about our people as we are about our mission.

Why Join Q2?

Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology-and we do that by empowering our people to help create success for our customers.

What Makes Q2 Special?

Being as passionate about our people as we are about our mission. We celebrate our employees in many ways through our year-round Q2 ChangeMakers awards program and global moments of recognition and connection. We invest in the growth and development of our team members through ongoing learning opportunities, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun and giving back together. From company-wide volunteer days to events like our Q2 Homecoming Week-featuring learning, community service, and culture-building experiences-we create opportunities to connect, grow, and make an impact.

SUMMARY

The Risk & Fraud team at Q2 helps our customers take a proactive stance against fraud while managing the risks inherent to their business. We build and enhance products that evolve with the ever-changing fraud landscape, delivering tangible value to our customers. Our solutions allow financial institutions to focus more of their time and energy on their mission: serving their customers and communities.

As a Machine Learning Engineer, you'll build and operate the production systems behind fraud detection at scale, helping protect nearly two trillion dollars in transactions for millions of users each year. That scale creates a rare opportunity: small improvements in model performance, latency, or reliability can have a meaningful impact on fraud losses for financial institutions and their customers. You'll work closely with data scientists and engineers to turn models into reliable, real-time systems and continuously improve how they perform in production.

You’ll gain hands-on experience working across model development, evaluation, deployment, and ongoing monitoring and improvements. This is an applied role: the software you build will be solving real problems for real customers, and will therefore need to be tested rigorously.

RESPONSIBILITIES

• Research emerging fraud and abuse patterns and translate that research into new detection approaches
• Help build next-generation ML products across identity, behavior, and transaction fraud, partnering directly with customers to understand their needs and shape product direction
• Build and optimize real-time , low-latency ML infrastructure, continually improving its reliability, scalability, and performance
• Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models, collaborating with data scientists to productionalize models into scalable applications
• Write clean, maintainable, and well-tested code, following production engineering best practices and leveraging the latest AI tooling
• Support monitoring and troubleshooting of production ML systems, including data pipelines and model performance

You are more likely to excel in the role if you:

• Enjoy autonomy in your work and feel a sense of ownership in the team's goals. You work quickly while keeping the big picture in mind
• Have empathy for the end user and a desire to measure your work by both the customer value and technical quality
• Maintain active interest in the latest ML developments and how they can be applied to solve business problems

EXPERIENCE AND KNOWLEDGE

• Bachelor's degree in related field and 2+ years of relevant experience
• Proven experience in ML model development and deployment
• Strong knowledge of statistics, optimization, probability theory, and experimental methodologies
• Proficiency in programming languages such as Python, R, or Java
• Experience with ML frameworks/libraries (TensorFlow, PyTorch, scikit-learn)
• Familiarity with cloud platforms and scalable computing resources
• Strong analytical, problem-solving, and collaboration skills

NICE TO HAVE

• Experience applying machine learning to fraud detection, risk modeling, or a closely related domain
• Experience building end-to-end ML systems, from data pipelines and model training through deployment and monitoring, including integrating models into applications at scale
• Experience building APIs, backend services, or working with distributed systems
• Experience working with large datasets or data processing frameworks
• Comfort using AI-assisted development tools (e.g., Claude Code, Copilot) to accelerate and improve engineering work

This position requires fluent written and oral communication in English.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this