About the Role
Risk Engineering team is part of Uber Core Technology organization. Our team is responsible for Risk decisions, fraud and abuse detection and prevention, Risk management decisions for tens of billions of USDs' of transactions on the Uber platform, and the number is growing rapidly.
We build cutting edge real-time fraud detection and prevention systems that scale to millions of users leveraging large scale data, distributed systems, and advanced machine learning technologies.
What the Candidate Will Do

• Design and develop scalable solutions to address fraud and abuse problems as well as drive better user experiences across Uber Products and Lines of Businesses
• Work with cross functional teams (Product Managers, Data Scientists/Analysts, Program
• engineers, Machine Learning Engineers) to identify any product/functionality gaps, define the problem space, contribute to product requirements, design and develop system solutions.
• Collaborate with other engineering teams in Uber Marketplace and Money to drive end-to-end design, execution and product rollout.
• Be an owner and strive for excellence in your role

Basic Qualifications

• At least three (3) years of software engineering experience
• Bachelor of Science (BS) in Computer Science, Physics, or Mathematics, or related field
• Experience in working on large-scale distributed systems
• Data-driven architecture and systems design
• Java/Go/Python
• Design and development of backend system/service APIs that scales to million of users.
• Streaming Platform Samza/Flink and building real-time streaming features
• Good Problem Solving skills. Taking on challenge problem and drive solution
• Experience shipping scalable, efficient, reliable, resilient code that reaches millions of users
• Ability to thrive in a fast paced environment with evolving requirements
• Customer obsession and user first mindset
• Collaborate on projects and working efficiently as part of a team

Preferred Qualifications

• Contribute to the wider software community through open source
• Master of Science (MS) in Computer Science or related Field.
• Previous experience with Risk, Fraud Detection and Machine Learning