Staff Machine Learning Engineer, Rider Pricing & Incentives

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

Staff Machine Learning Engineer at Uber focused on Rider Pricing & Incentives. The role involves optimizing pricing and promotion algorithms using advanced ML techniques like deep learning, generative AI, causal modeling, and reinforcement learning. Responsibilities include leading a team, improving model performance, owning the end-to-end problem, and mentoring junior members. The role requires experience with ML and optimization algorithms, large-scale data systems, and production-ready algorithmic systems.

What you'd actually do

  1. Lead 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, with 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, with 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) and 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
  • deep learning
  • generative AI
  • causal modeling
  • reinforcement learning
  • machine learning and optimization algorithms
  • large-scale data systems
  • production-ready algorithmic systems

Other signals

  • optimize pricing strategies
  • promotional systems
  • deep learning
  • generative AI
  • causal modeling
  • reinforcement learning