Software Engineer II - Earner Incentive

Uber Uber · Consumer · Seattle, WA +2 · Engineering

Software Engineer II on the Incentive Platform team at Uber, focusing on building and scaling a business-critical, ML-powered incentive ecosystem for drivers and couriers. The role involves architecting and developing high-throughput, real-time distributed systems and ML inference infrastructure to optimize incentive generation and delivery.

What you'd actually 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 product requirements and lead product rollout plans.

Skills

Required

  • 2+ years experience working on the full software life cycle including gathering requirements, project planning, solution design, coding/implementation, testing, rollout/deployment and best practices as an individual contributor.
  • Experience coding using general purpose programming language (eg. C/C++, Java, Python, Go, C#)
  • Strong collaboration, documentation and communication skills.

Nice to have

  • Experience building consumer-facing products.
  • Experience designing and implementing large-scale service with excellent quality.
  • Experience working with Machine Learning engineers and scientists on ML model deployment.
  • Experience collaborating with other engineers and non-tech stakeholders, both to meet short-term goals and to create long-term partnerships

What the JD emphasized

  • ML-powered incentive ecosystem
  • ML inference and optimization
  • ML model deployment

Other signals

  • ML-integrated backends
  • ML-powered incentive ecosystem
  • ML inference and optimization