Senior Machine Learning Engineer - Infra/Ops - Fraud
Senior Machine Learning Engineer building and scaling production fraud-detection systems, including feature pipelines, online inference, monitoring, and reliability capabilities. Requires 6+ years of experience with ML infrastructure and technologies such as Python, PyTorch, Spark, SageMaker, and Airflow.
About the job
Responsibilities
- Build and scale machine learning systems for fraud detection.
- Solve technical challenges across machine learning, data infrastructure, and production reliability.
- Develop foundational ML capabilities using financial network data to detect and prevent fraud.
- Own development of feature computation and online inference pipelines for production ML systems.
- Build observability, monitoring, and automated debugging capabilities.
- Collaborate with engineers, data scientists, ML infrastructure, and product teams on high-impact initiatives.
Requirements
- 6+ years of relevant experience building, deploying, and scaling production machine learning systems.
- Strong experience with ML infrastructure and operations, including production deployment, monitoring, and reliability.
- Ability to independently own and deliver complex, end-to-end ML engineering projects.
- Proficiency with Python.
- Experience with PyTorch, Spark, SageMaker, and Airflow.
Nice-to-haves
- Experience in fraud or risk domains.
- Experience with graph machine learning.
Compensation and benefits
- Annual salary range of $228,960–$315,360.
- Additional compensation may include equity and/or commission.
- Benefits include medical, dental, vision, and 401(k).
Skills
Python, PyTorch, Spark, Amazon Sagemaker, Apache Airflow, ML Infrastructure, Machine Learning Operations, Online Inference, Feature Engineering, Model Monitoring, Graph Machine Learning, Fraud Detection
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