Socure is seeking to solve the problem of identity fraud by building and scaling machine learning systems that extract risk and fraud insights from device and behavioral signals.
Requirements
5+ years of experience building and deploying machine learning systems in production.
Proficient in Java, or Go, with experience using production-grade ML frameworks.
Familiarity with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar tools.
Strong understanding of AWS services including SageMaker, Glue, EMR, Lambda, and Step Functions.
Experience with orchestration tools such as Airflow or AWS Step Functions.
Deep understanding of MLOps concepts including deployment automation and experiment tracking.
Solid knowledge of data structures, algorithms, and distributed computing.
Responsibilities
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.
Other
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field.
Strong problem-solving and communication skills.
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.
