Machine Learning Engineer

About the Opportunity

A rapidly growing technology company is seeking a Senior Machine Learning Engineer / Applied Scientist to help solve complex fraud detection and risk management challenges at scale. The company's machine learning team plays a central role in business operations, developing production models that influence millions of real-time decisions and help protect customers from fraudulent activity.

This role offers the opportunity to work on large-scale machine learning systems, apply advanced statistical methods, and develop new approaches to fraud detection and risk modeling. You'll have ownership across the machine learning lifecycle, from initial experimentation and validation to production deployment, while collaborating with experienced machine learning scientists and engineers.

Key Responsibilities

• Design, develop, and deploy machine learning models for fraud detection, risk assessment, and decision-making.

• Own the end-to-end machine learning lifecycle, including experimentation, model validation, deployment, monitoring, and ongoing improvement.

• Work with large datasets in distributed computing environments to develop scalable machine learning solutions.

• Build and improve model evaluation frameworks, performance metrics, and validation methods.

• Apply statistical techniques and experimental design to assess model performance and improve prediction quality.

• Develop machine learning models from the ground up, selecting appropriate approaches based on business requirements and data characteristics.

• Collaborate with cross-functional stakeholders to identify business challenges and deliver measurable improvements through machine learning.

• Contribute to production systems that support real-time decisions and help address evolving fraud and risk patterns.

Required Qualifications

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

• Strong programming skills in Python and SQL.

• Experience working with Apache Spark and distributed data processing environments.

• Deep understanding of machine learning methods, 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 modeling, payments, or trust and safety is required.

• Ability to translate complex business problems into practical machine learning solutions and evaluate their impact.

Preferred Qualifications

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

• Experience developing and operating machine learning systems that support large-scale, real-time business decisions.

Compensation and Benefits

The base salary ranges from $140,000 to $175,000, depending on location and other applicable factors.

The compensation package also includes an annual performance bonus, equity or stock options, unlimited paid time off, and a strong benefits package.

Work Arrangement and Eligibility

This is a fully remote position open to eligible candidates anywhere in the United States. Applicants must be U.S. citizens or Green Card holders.

Why Consider This Role?

This opportunity is suited to an experienced machine learning professional who wants to work on mission-critical fraud and risk problems at scale. The role offers significant technical ownership, visibility across the business, and the chance to develop machine learning solutions that directly influence customer protection and business outcomes.