Fraud Risk ML Engineer — Real-Time, Hybrid

Kubelt; New York (NY); Hybrid; USD 187,000 - 258,700; Full time

Benefits offered by this job

Unlimited time off

Flexible working arrangements

Comprehensive health, dental, and vision plans

401(k) with 4% company match

Equity option plan

Health and wellness spending

Team summits

Job summary

A leading fintech company in New York is seeking an experienced ML Engineer to develop scalable systems for fraud detection and anomaly detection. You will leverage your expertise in Python and apply your understanding of ML frameworks to create robust monitoring and alerting systems. The ideal candidate has over 5 years of experience and a strong track record in machine learning. This role offers comprehensive compensation, significant equity options, and a hybrid working model.

Qualifications

• 5+ years of experience building ML systems in production, including 2+ in fraud or anomaly detection.
• Degree in Computer Science, Engineering, Statistics, Applied Math, or related field.
• Proven track record designing and maintaining ML models at scale.
• Advanced proficiency in Python and ML frameworks like PyTorch, TensorFlow.

Responsibilities

• Architect and build scalable ML systems for fraud detection and anomaly detection.
• Develop and maintain ML pipelines from data ingestion to monitoring.
• Design low-latency real-time decision systems for fraud risk.
• Own ML infrastructure including automated model retraining.
• Build monitoring for model performance and data quality.

Job description

A leading fintech company in New York is seeking an experienced ML Engineer to develop scalable systems for fraud detection and anomaly detection. You will leverage your expertise in Python and apply your understanding of ML frameworks to create robust monitoring and alerting systems. The ideal candidate has over 5 years of experience and a strong track record in machine learning. This role offers comprehensive compensation, significant equity options, and a hybrid working model.