Senior Software Engineer, Machine Learning

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.

Why join us?

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

What you’ll do:

• 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.

What would make you a strong fit:

• 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.

Bonus points:

• 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.

A little about us:

Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Brands including DoorDash, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.