Senior Machine Learning Engineer (Fraud)
Ladders · United States
- On site
- Full-time
- United States
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About the role
For our client, we are seeking a Senior Machine Learning Engineer (fraud) to join the team of a leader in the Finance & Insurance space. This role will lead technical initiatives focused on scalable systems, platform reliability, and meaningful business transformation. You will work across engineering, product, operations, and business stakeholders to translate complex requirements into practical technology solutions. The position offers the opportunity to influence architecture, execution quality, and the technology capabilities that enable long-term growth within a regulated financial services environment.
Location: Remote - US based candidates only, no visa sponsorship available
Compensation: $153,000 – $213,000 annually
Responsibilities
Location: Remote - US based candidates only, no visa sponsorship available
Compensation: $153,000 – $213,000 annually
Responsibilities
- Lead the development of cutting-edge fraud prediction models using diverse data sources
- Build and scale robust feature pipelines and training datasets from both proprietary and third-party data
- Prototype new modeling ideas and conduct offline experiments to refine approaches
- Integrate models into decision systems, enhancing reliability and speed
- Monitor model health and establish workflows for retraining as fraud patterns change
- Identify and implement foundational improvements in model development processes
- Collaborate cross-functionally to define requirements and communicate technical results effectively
- 6+ years of experience with ML models; PhD may substitute for 2 years
- Proven success in deploying ML models in real-time environments
- Strong Python programming and production-quality code experience
- Expertise in tabular classification models, preferably with LightGBM/XGBoost
- Familiarity with deep learning frameworks, ideally PyTorch
- Experience in distributed data processing using Spark or similar
- Knowledge of ML lifecycle tools for orchestration and monitoring
- Comprehensive health care coverage with all premiums fully covered for employees and dependents
- Generous stipends for Technology, Food, and lifestyle needs through Flexible Spending Wallets
- Competitive vacation and holiday benefits to encourage work-life balance
- Employee stock purchase plan that allows participation in company equity at a discount