Direct Hire - Senior Machine Learning Engineer - Remote USA

Are you interested in working for a fast-growing, Series C fintech company focused on empowering the next generation with financial tools and education? With over 5.5 million users and $175M+ raised, the company is transforming how Gen Z banks and builds financial literacy.

Direct Hire - Senior Machine Learning Engineer - Remote

• No Third-Party Candidates are eligible for this position as it is a Direct Hire role.
• Candidates must complete the screening questions for consideration. Thank you.

Job Summary:

We're seeking a Senior Machine Learning Engineer with deep experience in Risk & Fraud detection systems. In this role, you'll lead the design, development, and deployment of ML models that help detect and prevent fraudulent activity in real-time.

Responsibilities:

• Build and deploy ML models to detect and mitigate fraud risk
• Own the technical roadmap for ML initiatives in Risk/Fraud
• Partner with Ops teams to respond to real-time fraud scenarios
• Use SQL to extract, transform, and analyze large datasets
• Write scalable, production-ready Python code for ML systems
• Design A/B tests and statistically sound experiments
• Work cross-functionally with Engineering, Product, and Risk teams

Required Qualifications:

• 5+ years of experience in Data Science or ML Engineering
• Strong Python and SQL skills
• Proven experience deploying ML models in production
• Experience designing fraud detection systems or risk models
• Strong communication skills (technical + non-technical audiences)

Preferred (Not Required):

• Experience in fintech, banking, or financial services
• Familiarity with real-time fraud mitigation or credit/lending systems

Perks & Benefits:

• Competitive base salary + equity
• Fully remote (U.S. only)
• Health, dental, and vision insurance
• Unlimited PTO & flexible schedule
• 401(k) with company match
• Paid parental leave

As part of the interview process, please answer the questions below and be prepared to share via email if selected for the initial screening process. Thank you. 

1. Tell me about a machine learning model you ve built specifically for fraud or risk detection. What was the business outcome, and how did you measure performance?

2. What tools and techniques have you used to deploy models into production? How do you monitor them once they re live?

3. Walk me through how you'd investigate a spike in fraud activity on a real-time platform. What data would you look at first, and how would you respond?