Senior Software Engineer, Sift

• If you're passionate about machine learning, cybersecurity, and creating safe online experiences, we’d love to hear from you!
• 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

Desirable

• Our team is responsible for defining the vision, conducting research, and developing advanced machine-learning models to tackle fraud at scale
• We focus on delivering clear and accurate risk assessments throughout the entire user journey—covering login attempts, account creation, and payment transactions
• By eliminating fraud losses and minimizing friction for legitimate users, we empower businesses to create seamless, trustworthy experiences
• We believe that machine learning is the key to preventing Account Creation Fraud, Account Takeover (ATO), and Payment Fraud
• Our solutions are designed to intelligently detect and mitigate risks, ensuring a secure and resilient online ecosystem
• At Sift, you’ll be at the forefront of AI/ML-driven fraud prevention, working with cutting-edge technologies to solve real-world problems
• You'll have the opportunity to innovate, develop industry-leading solutions, and make a tangible impact in securing the digital world
• 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