WHY SOCURE?

At Socure, we’re on a mission—to verify 100% of good identities in real time and eliminate identity fraud from the internet.
Using predictive analytics and advanced machine learning trained on billions of signals to power RiskOS, Socure has created the most accurate identity verification and fraud prevention platform in the world. Trusted by thousands of leading organizations—from top banks and fintechs to government agencies—we solve real, high-impact problems at scale. Come join us!

PREFERRED QUALIFICATIONS

• Experience working with device, behavioral or time-series data.
• Familiarity with feature stores and strategies for maintaining online/offline feature consistency.
• Exposure to large-scale data processing with Apache Spark, Kafka, or Flink.
• Experience with model observability tools and A/B testing frameworks.
• Knowledge of fraud detection, identity verification, or risk modeling domains.
• Exposure to LLMs, prompt engineering, or foundation model deployment is a plus.
  Socure is an equal opportunity employer and values diversity of all kinds at our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

ABOUT THE ROLE

Socure is seeking a highly motivated and experienced Senior Backend Engineer to join our Digital Intelligence team. This role involves designing, developing, and scaling machine learning systems that extract risk and fraud insights from device and behavioral signals. You will play a key role in operationalizing ML workflows in both batch and real-time settings, collaborating with cross-functional teams to deliver high-impact, production-ready models.
This role is dedicated to the Digital Intelligence team and will support our engineers and data scientists by delivering scalable, high-quality ML infrastructure and operational tooling for modeling and inference.

WHAT YOU’LL DO:

• Design and implement scalable ML pipelines for model training, inference, and evaluation.
• Build and maintain systems for feature engineering, deployment, and monitoring.
• Collaborate with data scientists to transform research prototypes into production-grade models.
• Optimize ML systems for performance, scalability, and cost-efficiency.
• Implement CI/CD and MLOps practices for model versioning, reproducibility, and monitoring.
• Manage ML workflows and pipeline orchestration.
• Partner with product, engineering, data science, and security teams to ensure compliant and reliable model delivery.
• Document systems and processes for team use and compliance.