Machine Learning Engineer

About the company Our client is a fintech company. The role Raydar is recruiting for this role on behalf of our client. Design, train and deploy machine learning models that make real-time risk calls in a payments setting, owning the data pipelines, evaluation and metrics behind them. The role leads modeling work as an individual contributor, with room to build and manage a team over time. What you'll do - Own the data pipelines and modeling design for core models, including a small language model used for contextual understanding. - Convert plain-language user approvals into structured, checkable limits covering vendors, amounts, items and time windows. - Score automated actions in real time to flag drift, substitutions, injection attempts and overspending before funds move. - Make risk calls from identity, user history, device and vendor signals, choosing whether to approve, request confirmation or refuse. - Own evaluation, labeling and metrics, tracking false approvals and false blocks as product measures. - Ship models into production paths with tight latency and cost budgets. Requirements What we're looking for - 2 to 5 years of experience in machine learning or related engineering work. - A hands-on record of training machine learning or language models, ideally including one that has operated live and driven consequential calls for users. - Proficiency in Python and TypeScript. - Sound judgment about where large language models are reliable, where they fail, and how to measure it. - A habit of designing evaluations before building models, and of shipping models they have built themselves. - Strong working knowledge in one specialty: scoring suspicious activity for fraud, building rule engines that grant or deny access, or testing and safeguarding language model outputs. - Comfort with a roughly six-day, in-office week (Sunday through Friday). Benefits Compensation and benefits - Base salary: USD 140,000 to 300,000 per year - Health, dental and vision coverage Location and work model - San Francisco, California, United States - On-site, roughly six days a week in office (Sunday through Friday), with flexibility on weekends - Full-time