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Uber
Senior Scientist, Rider Personalization
3+ months ago• San Francisco, CA
Viewed on February 1, 2026
Apply on company site
About Us
Uber is changing how people think about transportation, part of the logistical fabric of 600+ cities - giving people what they want when they want it.
- Size: 10000+ employees
- Industry: Technology
View Company Profile:
About the Role:
The Rider team is the centerpiece of the Uber consumer experience, owning the core flagship app used by over 100M+ monthly active users.
The Personalization team is looking for a Senior Scientist who can help us innovate on our AIML efforts and tackle the next frontier of challenges, including ranking & recommender systems, and agentic applications.
As a Scientist, you will play a critical role in enhancing the personalization AIML experience for millions of Rider app users worldwide. You will leverage your expertise in machine learning and data science to optimize our ranking & recommendation systems, help develop agentic applications, and simulate marketplace outcomes, ultimately improving user satisfaction and achieving business growth through one of the most impactful channels.
What You'll Do:
- Prototype new models and methodologies (e.g. evidential deep learning, reinforcement learning, listwise ranking) for improving performance across the various personalization AI/ML surfaces of the rider app.
- Deep dive the data and optimize current ranking algorithms to enhance the relevance and accuracy of ranking results.
- Design experiments and offline simulations to measure the performance of AIML systems, including agentic applications, interpret the results, and make impactful recommendations for areas of development
- Work closely with product managers, engineers, and other scientists to define project goals and deliver data-driven solutions.
- Stay current with the latest advancements in personalization AIML
- Mentor and review the work of junior team members
Basic Qualifications:
- M.S. or Bachelor's degree in Computer Science, Statistics, Economics, Mathematics, Operations Research, or other quantitative fields.
- 5+ years of industry experience as an Applied Scientist, Research Scientist, or equivalent.
- Proficiency in programming languages (Python, Py Spark, SQL) and ML frameworks (Tensor Flow, Py Torch, Scikit-Learn),
- Strong business and product sense: ability to find business opportunities in AIML Systems and help define product and engineering roadmaps to capture these opportunities.
Preferred Qualifications:
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Prior experience with feed ranking, recommender systems, or search algorithms
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Solid understanding of MLOps practices, including design documentation, testing, and source code management with Git.
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Advanced skills in the development and deployment of large-scale ML models and optimization algorithms
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For San Francisco, CA-based roles: The base salary range for this role is USD**$183,000 per year**
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USD**$203,000 per year**.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.
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By signing up, you agree to our 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.
Client-provided location(s): San Francisco, CAJob ID: Uber-144804Employment Type: FULL_TIMEPosted: 2025-07-16T00:29:47Apply on company site
Perks and Benefits
Health and Wellness
- Health Insurance
- Health Reimbursement Account
- Dental Insurance
- Vision Insurance
- Life Insurance
- FSA With Employer Contribution
- Fitness Subsidies
- On-Site Gym
- Mental Health Benefits
Parental Benefits
- Fertility Benefits
Work Flexibility
- Flexible Work Hours
- Remote Work Opportunities
- Hybrid Work Opportunities
Office Life and Perks
- Casual Dress
- Pet-friendly Office
- Snacks
- Some Meals Provided
- On-Site Cafeteria
Vacation and Time Off
- Paid Vacation
- Unlimited Paid Time Off
- Paid Holidays
- Personal/Sick Days
- Sabbatical
- Volunteer Time Off
Financial and Retirement
- 401(K)
- Company Equity
- Performance Bonus
Professional Development
- Work Visa Sponsorship
- Associate or Rotational Training Program
- Promote From Within
- Mentor Program
- Access to Online Courses
Diversity and Inclusion
- Employee Resource Groups (ERG)
- Diversity, Equity, and Inclusion Program
Apply on company site
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About Uber
Reviews
3.1
10 reviews
Work Life Balance
4.2
Compensation
2.3
Culture
3.5
Career
2.0
Management
2.5
45%
Recommend to a Friend
Pros
Flexible hours and schedule
Meeting different people and cultures
Make your own hours
Cons
Inconsistent and low pay
Safety concerns with passengers
Traffic and difficult drivers
Salary Ranges
23,534 data points
Junior/L3
Mid/L4
Senior/L5
Staff/L6
Junior/L3 · Data Scientist L3
0 reports
$145,456
total / year
Base
-
Stock
-
Bonus
-
$123,638
$167,274
Interview Experience
5 interviews
Difficulty
3.0
/ 5
Duration
14-28 weeks
Offer Rate
40%
Experience
Positive 80%
Neutral 20%
Negative 0%
Interview Process
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
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Case Study
Technical Knowledge
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