> Markdown version of [/jobs/ext/2715492-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2715492-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Q2 Software, Inc. - **Location:** Cary, NC, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Big Data, Cloud Computing, Distributed Systems, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Tensorflow, Real Time Systems, Pytorch, Backend, Scikit Learn, Code Testing, Integration Frameworks, Machine Learning Operations, Api Design, Data Pipelines - **Published:** September 4, 2026 - **Apply:** https://q2ebanking.wd5.myworkdayjobs.com/Q2/job/Cary-North-Carolina/Machine-Learning-Engineer_REQ-12736 ## About the Role * 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 time. ## Description 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 ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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