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