- Raydar
- San Francisco, CA
- Full-Time
- 5 days ago
- $140,000–$300,000 / year
Machine Learning Engineer.
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Machine Learning Engineer: our view in 3 lines...
- The Role:This position suits people who build models that judge risk in financial transactions as they happen.
- The Person:The person will own data pipelines and modeling for core risk models, score automated actions in real time, make approve or refuse risk decisions, track false approvals and false blocks, and ship models into production.
- Requirements:The ideal candidate has 2 to 5 years of experience in machine learning or related engineering work, proficiency in Python and TypeScript, and a hands on record of training machine learning or language models.
About the role
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