採用
必須スキル
Python
Java
Go
Spark
Machine Learning
About the Team:
Uber Marketplace is at the heart of Uber's business, with Rider Pricing & Incentives playing a critical role by setting prices and targeting promotions for Uber riders. Our team's mission is to drive revenue growth, ridership growth and Uber's profitability through advanced machine learning and data science. We build reliable, scalable platforms that optimize rider pricing, real-time and offline promotions, including discounts, and personalized messaging.
The Rider Pricing & Incentives team is a fast-moving, high-opportunity space where you'll have the chance to make a significant impact on the business. You'll take ownership of one key pillar of the promotions or rider pricing domain and lead the technical direction for improving our pricing algorithms, promotion algorithms and models. You will work across ML, serving, and optimization system components, and set technical direction for modeling best practices across model building, evaluation and deployment.
About the Role:
In this role, you'll apply advanced machine learning technologies-including deep learning, generative AI for personalized communications, causal modeling, and reinforcement learning-to optimize pricing strategies and promotional systems. You will also work with serving infrastructure and product teams to design and evolve the rider pricing and promotions systems to support new product and algorithm evolutions, promotion use cases and drive Uber's top-line rider and revenue growth.
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What You'll Do
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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.
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Improve the performance of models and algorithms powering pricing algorithms and promotion targeting.
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Own the problem E2E, including working with cross-functional teams to define the product and/or technical roadmap.
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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.
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Basic Qualifications
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Masters degree in Computer Science, Engineering, Mathematics, or a related field, with 7+ years of full-time engineering experience.
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Proficiency in one or more programming languages (e.g., C, C++, Java, Python, Go).
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Experience with machine learning and optimization algorithms.
Preferred Qualifications:
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PhD in Computer Science, Engineering, Mathematics, or a related field, with 2+ years of full-time engineering experience.
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Experience solving complex business problems by translating them into machine learning and optimization solutions.
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Familiarity with large-scale data systems (e.g., Spark, Hive) and experience building production-ready algorithmic systems.
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Strong background in deep learning, generative AI, causal modeling, and reinforcement learning.
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For Sunnyvale, CA-based roles: The base salary range for this role is USD**$232,000 per year**
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USD**$258,000 per year**.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.
Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
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Uberについて

Uber
PublicUber develops, markets, and operates a ride-sharing mobile application that allows consumers to submit a trip request.
10,001+
従業員数
San Francisco
本社所在地
$120B
企業価値
レビュー
3.7
10件のレビュー
ワークライフバランス
3.2
報酬
4.0
企業文化
4.1
キャリア
3.4
経営陣
2.8
68%
友人に勧める
良い点
Good compensation and pay
Flexible hours and schedule
Great team culture and colleagues
改善点
Long hours and tight deadlines
High pressure and stressful environment
Poor management and lack of support
給与レンジ
15,354件のデータ
Junior/L3
Mid/L4
Senior/L5
Staff/L6
Junior/L3 · Data Scientist L3
0件のレポート
$145,456
年収総額
基本給
-
ストック
-
ボーナス
-
$123,638
$167,274
面接体験
5件の面接
難易度
3.0
/ 5
期間
14-28週間
内定率
40%
体験
ポジティブ 80%
普通 20%
ネガティブ 0%
面接プロセス
1
Application Review
2
Online Assessment
3
Recruiter Screen
4
Technical Phone Screen
5
Case Study/Analytics Test
6
Final Loop/Panel Interview
7
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Case Study
Technical Knowledge
ニュース&話題
Uber Eats now offers easier returns with ‘instant’ refunds — but it will actually cost you - New York Post
New York Post
News
·
3d ago
Mom Sues Uber Over ‘Terrifying’ Ride with Kids After Driver Allegedly Refused to Let Them Out and Became Violent - People.com
People.com
News
·
4d ago
I'm an ex-Wall Street trader who drives for Uber and Lyft. Gas prices have me rethinking which trips I take. - Business Insider
Business Insider
News
·
4d ago
Uber Raises Delivery Hero Stake in €270 Million Prosus Deal - Bloomberg.com
Bloomberg.com
News
·
4d ago