Machine Learning Engineer
About the Company
The hiring company is a rapidly growing technology business focused on solving complex fraud and risk challenges at scale. Its machine learning team develops production models that influence millions of real-time decisions and help protect customers from fraudulent activity.
About the Role
The Senior Machine Learning Engineer / Applied Scientist will develop and deploy machine learning models for fraud detection and risk decisioning. This role offers ownership across the full machine learning lifecycle, from experimentation and model validation to production deployment.
You'll work with large datasets and distributed computing environments alongside experienced machine learning scientists and engineers. The focus is on developing effective models, improving evaluation methods, and applying advanced statistical and machine learning techniques to real-world business challenges.
Key Responsibilities
•Design, develop, and deploy machine learning models for fraud detection and risk decisioning.
•Manage the full machine learning lifecycle, including experimentation, validation, deployment, and ongoing improvement.
•Analyze and process large-scale datasets using distributed computing environments.
•Develop and refine model evaluation frameworks, performance metrics, and validation methods.
•Apply statistical and machine learning techniques to address complex fraud and risk problems.
•Collaborate with cross-functional stakeholders to improve model performance and deliver measurable business outcomes.
Required Qualifications
•At least 4 years of hands-on machine learning experience in a production environment.
•Strong proficiency in Python, SQL, and Spark.
•Deep understanding of machine learning, statistics, and experimental design.
•Experience developing machine learning models from the ground up, rather than relying solely on existing frameworks.
•Professional experience in fraud detection, risk, payments, or trust and safety is required.
•Must be a U.S. citizen or Green Card holder.
Preferred Qualifications
•Master's or PhD degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
Compensation and Benefits
•Base salary of $140,000–$175,000 annually, depending on location.
•Annual performance bonus.
•Equity or stock options.
•Unlimited paid time off.
•Benefits package.
Work Environment
This is a fully remote opportunity open to candidates anywhere in the United States. The role offers substantial ownership, visibility, and the opportunity to contribute to machine learning systems that are central to the company's fraud prevention and risk management operations.