Senior Machine Learning Engineer - Rider Pricing & Incentives

Uber Uber · Consumer · Sunnyvale, CA · Engineering

Senior Machine Learning Engineer at Uber focused on Rider Pricing & Incentives. The role involves developing and implementing advanced ML and optimization techniques to optimize pricing strategies and promotional systems, driving revenue and ridership growth. It requires end-to-end ownership, collaboration with cross-functional teams, and mentoring junior members. The role utilizes deep learning, generative AI, causal modeling, and reinforcement learning.

What you'd actually do

  1. Take a lead on pricing ML and optimization problems, by developing and implementing 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.
  5. Collaborate with cross-functional teams to ensure alignment and drive Uber’s ridership and revenue growth.

Skills

Required

  • Masters degree in Computer Science, Engineering, Mathematics, or a related field, with 5+ 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
  • production-ready algorithmic systems

Other signals

  • optimize rider pricing
  • promotions algorithms
  • deep learning
  • generative AI
  • causal modeling
  • reinforcement learning