Machine Learning Engineer

Research emerging fraud and abuse patterns and translate research into new detection approaches Help build next-generation ML products across identity, behavior, and transaction fraud Partner directly with customers to understand their needs and shape product direction Build and optimize real-time, low-latency ML infrastructure Improve infrastructure reliability, scalability, and performance Build and maintain systems and pipelines supporting model training, evaluation, and inference Collaborate with data scientists to productionalize models into scalable applications Write clean, maintainable, and well-tested code using production engineering best practices and AI tooling Monitor and troubleshoot production ML systems, data pipelines, and model performance Requirements 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 Fluent written and oral communication in English Applicants must be authorized to work for any employer in the U.S. Q2 is unable to sponsor or take over sponsorship of an employment Visa at this time Core Competencies Demonstrates expertise in machine learning model development and deployment, with a strong foundation in statistics and optimization. Proficient in building scalable ML infrastructure and collaborating effectively with cross-functional teams to enhance product offerings. Highest-signal resume keywords Machine Learning Model Development Python Programming TensorFlow Framework Real-Time ML Infrastructure Statistical Analysis Hard Skills Machine Learning Model Deployment Statistics Optimization Probability Theory Experimental Methodologies R Programming Java Programming Data Pipeline Management Model Performance Monitoring Soft Skills Analytical Skills Problem-Solving Collaboration Communication Industry Keywords Fraud Detection Abuse Patterns Scalable Computing Model Training Model Evaluation Model Inference Tools & Technologies PyTorch Scikit-learn Cloud Platforms AI Tooling #J-18808-Ljbffr