Senior Machine Learning Engineer I // II

Role overview Own technical delivery for production machine learning projects that support fraud detection in online commerce. The work spans offline experimentation, model and pipeline development, deployment, and ongoing monitoring, with a focus on improving performance while keeping systems scalable and evaluation scientifically sound. You’ll partner with product, engineering, and risk colleagues to turn business needs into robust modeling solutions.

Responsibilities - Identify, prototype, and integrate modeling methods and infrastructure that improve fraud detection and scale. - Design and build machine learning pipeline components that make experimentation faster and more reliable. - Take high-impact projects from research or prototype through deployment and production operation. - Evaluate model performance and strengthen experimentation practices using sound statistical methods. - Work with product, engineering, and risk teams to translate customer and business needs into technical work. - Promote strong practices in testing, documentation, model monitoring, and software development.

Requirements - Degree in computer science, statistics, or a comparable quantitative field. - At least four to six years of post-degree experience in a production machine learning environment. - Strong grounding in machine learning theory and statistical evaluation, including supervised or unsupervised methods at scale. - Track record of moving machine learning work into high-scale production systems. - Proficiency with Python, SQL, major machine learning libraries, and Spark. - Ability to explain technical findings to both technical colleagues and nontechnical stakeholders, with attention to business outcomes.

Nice to have - Experience in fraud prevention, financial technology, payments, or e-commerce. - Interest in writing well-tested production code. - Master’s degree or doctorate.

Benefits and work setup - The role is listed as remote. U.S. benefits include health, dental, and vision coverage; retirement plan matching; stock options; performance bonus eligibility; paid parental leave; paid time off; disability and life insurance; and a learning budget.