{"schemaVersion":"jobsearcher.job.v1","id":"67aa374183fc84f40e6918f6","url":"https://jobsearcher.com/jobs/67aa374183fc84f40e6918f6","canonicalUrl":"https://jobsearcher.com/jobs/67aa374183fc84f40e6918f6","title":"Machine Learning Engineer","description":"The Risk & Fraud team helps 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 customers. Our solutions allow financial institutions to focus more of their time and energy on serving their customers and communities.As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You'll work closely with data scientists and engineers to bring models into production, ensuring they are reliable, scalable, and maintainable.You'll gain hands-on experience working across model development, evaluation, deployment, monitoring, and ongoing improvements. This is an applied engineering role — the software you build will solve real-world problems and must be production-ready, reliable, and testable.A Typical DayYour Key ResponsibilitiesBuild and maintain systems and pipelines that support training, evaluation, and inference for machine learning modelsContribute to deploying machine learning models into production environments and ensuring they run reliably at scaleWrite clean, maintainable, and well-tested code following production engineering best practicesSupport monitoring and troubleshooting production ML systems, including data pipelines and model performanceCollaborate with data scientists and engineers to productionalize models and integrate them into scalable applicationsHelp improve the reliability, scalability, and performance of ML systems over timeContribute to improving tooling and infrastructure that supports the ML development lifecycleYou Are More Likely to Excel If You:Enjoy autonomy in your work and take ownership of team goals while balancing speed with long-term impactHave empathy for end users and measure success through both customer value and technical qualityAre enthusiastic about machine learning, engineering excellence, and continuous professional developmentBring Your Passion, Do What You Love. Here's What We're Looking ForMust-HavesBachelor's degree in a relevant field with 2+ years of related experience, or equivalent practical experienceProficiency in PythonExperience writing clean, maintainable code and using version control tools such as GitExperience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learnNice to HaveExperience building end-to-end ML systems, including data pipelines, model training, deployment, and monitoringExperience deploying or integrating machine learning models into applicationsExperience building APIs, backend services, or working with distributed systemsFamiliarity with cloud platforms such as AWS, GCP, or AzureExposure to MLOps concepts such as CI/CD and model monitoringExperience working with large datasets or data processing frameworksExperience with additional programming languages such as TypeScript","company":"Extendmyteam","rawCompany":"extendmyteam","city":"Austin","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-06-26T02:22:02.986Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"The Risk & Fraud team helps 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 customers. Our solutions allow financial institutions to focus more of their time and energy on serving their customers and communities.As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You'll work closely with data scientists and engineers to bring models into production, ensuring they are reliable, scalable, and maintainable.You'll gain hands-on experience working across model development, evaluation, deployment, monitoring, and ongoing improvements. This is an applied engineering role — the software you build will solve real-world problems and must be production-ready, reliable, and testable.A Typical DayYour Key ResponsibilitiesBuild and maintain systems and pipelines that support training, evaluation, and inference for machine learning modelsContribute to deploying machine learning models into production environments and ensuring they run reliably at scaleWrite clean, maintainable, and well-tested code following production engineering best practicesSupport monitoring and troubleshooting production ML systems, including data pipelines and model performanceCollaborate with data scientists and engineers to productionalize models and integrate them into scalable applicationsHelp improve the reliability, scalability, and performance of ML systems over timeContribute to improving tooling and infrastructure that supports the ML development lifecycleYou Are More Likely to Excel If You:Enjoy autonomy in your work and take ownership of team goals while balancing speed with long-term impactHave empathy for end users and measure success through both customer value and technical qualityAre enthusiastic about machine learning, engineering excellence, and continuous professional developmentBring Your Passion, Do What You Love. Here's What We're Looking ForMust-HavesBachelor's degree in a relevant field with 2+ years of related experience, or equivalent practical experienceProficiency in PythonExperience writing clean, maintainable code and using version control tools such as GitExperience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learnNice to HaveExperience building end-to-end ML systems, including data pipelines, model training, deployment, and monitoringExperience deploying or integrating machine learning models into applicationsExperience building APIs, backend services, or working with distributed systemsFamiliarity with cloud platforms such as AWS, GCP, or AzureExposure to MLOps concepts such as CI/CD and model monitoringExperience working with large datasets or data processing frameworksExperience with additional programming languages such as TypeScript","datePosted":"2026-06-26T02:22:02.986Z","dateModified":"2026-06-26T02:22:02.986Z","hiringOrganization":{"@type":"Organization","name":"Extendmyteam","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Austin","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"67aa374183fc84f40e6918f6"},"url":"https://jobsearcher.com/jobs/67aa374183fc84f40e6918f6"}}