Machine Learning Engineer - WFH

About the Company

The company is a rapidly growing technology business tackling complex fraud detection and risk management challenges at scale. Its machine learning team develops production models that support millions of real-time decisions and help protect customers from fraud.

About the Role

The company is seeking a Senior Machine Learning Engineer / Applied Scientist to develop and deploy machine learning solutions for fraud detection and risk decisioning. This is a hands-on role within a highly technical team, with responsibility for the full machine learning lifecycle, from experimentation and validation through production deployment.

You’ll work with large datasets and distributed computing environments, applying statistical and machine learning techniques to solve challenging fraud and risk problems. The role also involves collaborating with experienced machine learning scientists, engineers, and cross-functional stakeholders to improve model performance and deliver measurable business results.

Key Responsibilities

• Design, develop, and deploy machine learning models for fraud detection and risk decisioning.

• Own the full machine learning lifecycle, including experimentation, model validation, and production deployment.

• Work with massive datasets in distributed computing environments.

• Develop and improve model evaluation frameworks and performance metrics.

• Apply statistical methods, machine learning techniques, and experimental design to address complex business problems.

• Build models from the ground up and develop approaches tailored to fraud and risk challenges.

• Collaborate with cross-functional stakeholders to improve model effectiveness and drive measurable business impact.

• Contribute to production machine learning systems that support real-time decisions and customer fraud protection.

Required Qualifications

• At least 4 years of hands-on machine learning experience in a production environment.

• Strong experience with Python, SQL, and Spark.

• Deep understanding of machine learning, statistics, and experimental design.

• Experience building machine learning models from the ground up, rather than only implementing existing frameworks.

• Required domain experience in fraud, risk, payments, or trust and safety.

• U.S. citizenship or Green Card status, as specified for this position.

Preferred Qualifications

• A PhD or master’s degree in Computer Science, Statistics, Mathematics, or a related quantitative field is highly preferred.

Salary & Benefits

• Base salary of $140,000–$175,000, depending on location.

• Annual performance bonus.

• Equity or stock options.

• Unlimited paid time off (PTO).

• Benefits package.

Work Arrangement

• Fully remote position available anywhere in the United States.

Why Consider This Role

This position offers the opportunity to work on mission-critical machine learning systems where model performance directly affects business decisions and fraud prevention. You’ll have substantial ownership across experimentation and production, work alongside experienced ML professionals, and contribute to a business where machine learning is a core function.