Software Engineer II - Earner Incentive

Software Engineer II - Earner Incentive Department: Engineering Team: Backend Location: Toronto, Canada Type: Full-Time **About the Role** Uber is looking for a Software Engineer to join our Incentive Platform team. This team works at the core of Uber’s marketplace, ensuring that drivers and couriers are incentivized to stay engaged on the platform. Generating and delivering the right incentives is one of Uber’s most complex engineering challenges, requiring a sophisticated orchestration between dynamic marketplace signals and a seamless driver experience. The platform requires expertise in high-scale distributed processing, micro-services, and machine learning infrastructure. As an Engineer on the team, you’ll play a key role in evolving our systems, driving efficiency, and pioneering Uber’s next-generation ML-powered incentive ecosystem. This is a rare opportunity to own a business-critical domain with massive global impact." Some of the problems you’ll be working on include: **Scaling a Global Incentive Engine**: Design and develop a high-throughput platform that powers millions of incentives across thousands of cities in real-time. **Extensible Architecture**: Build modular, extensible systems that allow our incentive mechanisms to evolve. **ML-Integrated Backends**: Build the infrastructure that makes our algorithms effective. You’ll create robust workflows for ML inference and optimization, directly influencing how incentives are generated and delivered at Uber scale. What the you will do ---- 1. As a backend engineer, you will architect, design and build software solutions to help with all aspects of capacity planning/management/engineering to scale Uber’s infrastructure across a variety of sophisticated workflows and business processes. 2. Design end-to-end features and systems to build high quality consumer-facing products. 3. Write code, test, and maintain production services for high availability, reliability, and performance. 4. Work with ML engineers and scientists to develop ML models to improve product performance. 5. Work with Product Managers to understand p