Senior Software Engineer, Machine Learning

Summary

Join Sift's team responsible for developing advanced machine-learning models to combat fraud at scale. You will research and apply cutting-edge machine learning algorithms, build and scale ML pipelines, and collaborate with other teams to create innovative fraud prevention solutions. This role requires a strong understanding of machine learning and data science, along with experience working with large datasets and production ML systems. You'll be at the forefront of AI/ML-driven fraud prevention, working with cutting-edge technologies to solve real-world problems and make a tangible impact in securing the digital world. If you are passionate about machine learning, cybersecurity, and creating safe online experiences, this is the ideal opportunity for you.

Requirements

• Practical understanding of machine learning and data science concepts, and a track record of solving problems with these methods
• 4+ years of experience working with production ML systems
• 3+ years experience working with large datasets using Spark, MapReduce, or similar technologies
• 5+ years experience building backend systems using Java, Scala, Python, or other language
• Experience training machine learning models end-to-end
• Strong communication & collaboration skills, and a belief that team output is more important than individual output
• Degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field

Responsibilities

• Research and apply the latest machine learning algorithms to power our core business product
• Build offline experimentation systems used to evaluate tens of thousands of models simultaneously
• Work on evolving Sift’s ML models and architecture
• End-to-end design & prototyping of a wide range of technologies
• Scale machine learning pipelines are used to produce thousands of models derived from terabytes of data
• Build systems that automatically explain how a model arrived at a prediction
• Use data science techniques to analyze fraudulent behavior patterns
• Collaborate with other teams to build new ways to use machine learning within Sift
• Generate and execute ideas to provide customers with meaningful and actionable insights to identify and prevent fraudulent behaviors and transactions
• Leverage anomaly detection algorithms to identify unusual behaviors for customer traffic patterns

Preferred Qualifications

• Experience working with scalable, real-time prediction systems in production
• Familiarity with multiple machine learning or statistical packages in Python or another programming language
• Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field