Senior Machine Learning Engineer, Risk

$84,500 - $204,600/Yr

PayPal - San Jose, CA

Full-time - Mid Level

Hybrid - San Jose, CA

Credit Intermediation and Related Activities

About the position

We are looking for a skilled Machine Learning Engineer with a strong background in both machine learning and software engineering to join our team. In this role, you will be responsible for designing, developing, and deploying ML solutions aimed at mitigating risks in PayPal transactions while optimizing performance and computational efficiency. You will collaborate closely with global, cross-functional teams spanning Business and Technology domains to address complex and dynamic business challenges.

Responsibilities

• Collaborate effectively with data scientists, analysts, and other stakeholders to understand data requirements, deliver innovative solutions, and ensure the availability, reliability, and scalability of the ML platform.
• Develop clean, high-performance, and maintainable code that meets quality standards.
• Utilize big data platforms to analyze complex issues and create actionable plans for resolution or solution optimization.
• Design and implement large-scale distributed systems to support the end-to-end machine learning (ML) lifecycle.
• Foster seamless collaboration with cross-functional teams across Business and Technology domains to align project objectives, gather requirements, and achieve successful outcomes.

Requirements

• 5-7 Years of related experience
• BS/MS Computer Science or Equivalent Degree
• Strong understanding of machine learning concepts, algorithms, and techniques, with hands-on experience in developing and deploying ML models.
• Proficient in multiple programming and scripting languages, including Unix/Linux Shell Scripting, Python, Java, and Scala.
• Extensive experience with big data technologies such as Hadoop, Spark, HBase, and Kafka.
• Demonstrated expertise in distributed systems, data streaming, complex event processing, and NoSQL solutions, with a track record of building and managing data integration pipelines for both batch and real-time data requirements.
• Proficient in cloud platforms (e.g., GCP, AWS, Azure) and containerization technologies such as Docker and Kubernetes.
• Strong knowledge of machine learning libraries and frameworks, including TensorFlow, PyTorch, and scikit-learn.
• Exceptional problem-solving skills, with the ability to leverage big data platforms for in-depth analysis and root cause identification of production issues.
• Familiarity and experience with Generative AI (GenAI) technologies is a significant advantage.

Benefits

• Flexible work environment
• Employee shares options
• Health and life insurance
• Annual performance bonus
• Equity
• Medical, dental, vision, and other benefits