Grid
Machine Learning Engineer
About this role
Grid, a fintech startup on a mission to democratize financial services, is hiring a Machine Learning Engineer in Seattle to build fraud detection, risk underwriting, and predictive analytics systems. You'll partner with product and engineering teams to leverage robust datasets and drive measurable business impact on lending and payment products.
What you'll do
- Design and deploy fraud detection and prevention models for financial transactions
- Build predictive analytics systems for payout and repayment optimization
- Develop risk underwriting models for lending and advance programs
- Collaborate with product and business stakeholders to identify high-impact ML opportunities
- Establish best practices and standards for statistical inference across the organization
- Leverage Grid's data warehousing and streaming infrastructure for model development
What they're looking for
- Machine learning model development and evaluation
- Python programming
- SQL and BigQuery
- Fraud detection techniques
- Predictive modeling and analytics
- Data pipeline and feature engineering
- Cloud platforms (GCP experience preferred)
- Communication with non-technical stakeholders
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Grid
Grid builds PayBoost, an AI-powered tax optimization product serving millions of Americans. The company is hiring engineers and product engineers to scale its platform, improve system architecture, and enhance reliability for mission-critical financial services.
View all jobs at GridLikely interview questions
- Walk us through a fraud detection or risk model you've built—what were the key challenges and how did you measure success?
- How would you approach building a predictive model for loan repayment likelihood with limited historical data?