Senior Software Engineer - Marketplace Real Time Supply Intelligence

Uber Uber · Consumer · San Francisco, CA +1 · Engineering

This role focuses on designing and scaling real-time dynamic pricing systems for Uber, involving large-scale, low-latency systems that handle millions of QPS. It requires expertise in distributed systems, optimization, and ML, with a focus on scaling model inference and real-time feature serving for pricing decisions.

What you'd actually do

  1. Partner with Marketplace Product and science teams to build a unified solution for pricing algorithms iterations and marketplace optimization.
  2. Collaborate with ML Engineering teams to build the MVP business product and scale model inference and real-time feature serving for pricing decisions
  3. Partner with Platform Engineering teams to ensure 99.99% availability for critical pricing systems, and contribute to multi-quarter 'Pricing as a Platform' initiatives to enhance marketplace efficiency, pricing reliability, and developer velocity.
  4. Mentor junior engineers and contribute to raising the technical bar across your immediate team and adjacent pricing teams.

Skills

Required

  • Computer Science degree or equivalent technical education
  • 4+ years of industry experience
  • Expert-level experience with large-scale distributed systems
  • Proficiency in real-time streaming or batch processing
  • Advanced knowledge of database technologies
  • Experience with microservices architecture
  • Exceptional coding skills in Go, Java and python

Nice to have

  • Strong background in real-time system, math, optimization and simulation.
  • Worked on real-time infrastructure serving 100M+ users, or 1M+ QPS
  • Fundamental machine learning knowledge
  • Track record of operational excellence: incident management, SLA optimization, and system reliability

What the JD emphasized

  • real-time dynamic pricing systems
  • large-scale, low-latency systems
  • millions of QPS
  • real-time distributed systems
  • ML modeling
  • model inference
  • real-time feature serving
  • 99.99% availability

Other signals

  • large-scale, low-latency systems
  • millions of QPS
  • real-time distributed systems
  • ML modeling
  • model inference
  • real-time feature serving