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

• 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 (Tensor Flow, 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

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.

• Machine Learning Model Development
• Python Programming
• Tensor Flow Framework
• Real-Time ML Infrastructure
• Statistical Analysis

• Machine Learning
• Model Deployment
• Statistics
• Optimization
• Probability Theory
• Experimental Methodologies
• R Programming
• Java Programming
• Data Pipeline Management
• Model Performance Monitoring

• Analytical Skills
• Problem-Solving
• Collaboration
• Communication

• Fraud Detection
• Abuse Patterns
• Scalable Computing
• Model Training
• Model Evaluation
• Model Inference

• Py Torch
• Scikit-learn
• Cloud Platforms
• AI Tooling