Staff Machine Learning Engineer

Uber

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Sunnyvale, CASan Francisco, CANew York, NY
Posted
4 days ago
Freshness
Confirmed live today

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How this pay compares to similar roles

Similar $231k
$173k $294k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $207,825–$254,750.

Based on 240 similar postings.

Employer

About Uber

Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.

Uber currently has 107 open roles on FindRole.

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At a glance

TL;DR · Staff Machine Learning Engineer

The Staff Machine Learning Engineer joins the Rider Pricing team to drive revenue and ridership growth through advanced machine learning and data science. In this role, you will lead a group of engineers to develop and implement new machine learning and optimization techniques for baseline trip pricing and targeted discounts. You will own key pillars of the rider pricing domain, setting technical direction for modeling best practices, evaluation, and deployment across ML, serving, and optimization system components. You will utilize deep learning, generative AI for causal modeling, representation learning, personalization, and reinforcement learning. Required skills include proficiency in C, C++, Java, Python, or Go, along with experience in large-scale data systems like Spark and Hive to solve complex business problems.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

See salary data

What you'll do

  • Lead a team of software engineers and machine learning engineers to develop pricing and optimization techniques.
  • Improve the performance of models and algorithms powering pricing and promotion targeting.
  • Own the end-to-end problem space including defining the product and technical roadmaps.
  • Apply deep learning, generative AI, and causal modeling to optimize rider pricing.
  • Design and evolve rider pricing systems to support new product and algorithm evolutions.
  • Mentor junior team members by role modeling machine learning best practices.
  • Set technical direction for modeling best practices across model building, evaluation, and deployment.

What we're looking for

  • Master's degree in Computer Science, Engineering, Mathematics, or a related field.
  • 7+ years of full-time engineering experience.
  • Proficiency in one or more programming languages such as C, C++, Java, Python, or Go.
  • Experience with machine learning and optimization algorithms.
  • PhD in Computer Science, Engineering, Mathematics, or a related field, with 2+ years of full-time engineering experience (preferred).
  • Experience solving complex business problems by translating them into machine learning and optimization solutions (preferred).
  • Familiarity with large-scale data systems and experience building production-ready algorithmic systems (preferred).
  • Strong background in deep learning, generative AI, causal modeling, and reinforcement learning (preferred).

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