Engineering Manager Ii, Machine Learning – Rider Pricing & Incentives

Uber Uber · Consumer · Sunnyvale, CA · Engineering

Engineering Manager II, Machine Learning for Uber's Rider Pricing & Incentives team. This role involves managing a team of SWEs and MLEs to develop and implement ML and optimization techniques for pricing and promotions, impacting billions of rides globally. Responsibilities include improving model performance, owning the end-to-end product/technical roadmap, and mentoring junior team members. Requires a Masters degree in a related field with 7+ years of experience, proficiency in programming languages, and experience with ML and optimization algorithms. Preferred qualifications include a PhD, experience with large-scale data systems, deep learning, generative AI, causal modeling, and reinforcement learning.

What you'd actually do

  1. Manage a group of SWEs and MLEs working on rider pricing and promotions to develop and implement new machine learning and optimization techniques powering billions of rides around the world, and helping riders achieve their mobility needs.
  2. Improve the performance of models and algorithms powering pricing algorithms and promotion targeting.
  3. Own the problem E2E, including working with cross-functional teams to define the product and/or technical roadmap.
  4. Mentor more junior team members by role modeling ML best practices. Collaborate with cross-functional teams to ensure alignment and drive Uber’s ridership and revenue growth. Help Uber’s end-users by making mobility options accessible and affordable.

Skills

Required

  • Masters degree in Computer Science, Engineering, Mathematics, or a related field
  • 7+ years of full-time engineering experience
  • Proficiency in one or more programming languages (e.g., C, C++, Java, Python, Go)
  • Experience with machine learning and optimization algorithms

Nice to have

  • PhD in Computer Science, Engineering, Mathematics, or a related field
  • 2+ years of full-time engineering experience
  • Experience solving complex business problems by translating them into machine learning and optimization solutions
  • Familiarity with large-scale data systems (e.g., Spark, Hive)
  • Experience building production-ready algorithmic systems
  • Strong background in deep learning, generative AI, causal modeling, and reinforcement learning

What the JD emphasized

  • advanced machine learning technologies
  • deep learning
  • generative AI
  • causal modeling
  • reinforcement learning
  • machine learning and optimization algorithms
  • large-scale data systems
  • production-ready algorithmic systems

Other signals

  • optimize rider pricing
  • promotion algorithms
  • personalized messaging
  • deep learning
  • generative AI
  • causal modeling
  • reinforcement learning