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About the Role
The team is Uber's Machine Learning Platform (Michelangelo).
We are building a truly extensible machine learning ecosystem. This system provides a comprehensive tool chains to empower ML engineers to build high quality machine learning solutions in all perspectives in Uber's business.
Our team is composed of collaborative people with deep knowledge in various domains, such as, but not limited to, distributed systems, analytical systems, large-scale backend services, large-scale computing infra, feature stores, data vis and cloud-based AI solutions, modeling techniques or critical, very large scale online system operations.
[Meet Michelangelo: Uber's Machine Learning Platform (http://eng.uber.com/michelangelo/)
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What the Candidate Will Do
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Build, test and manage the micro services and libraries
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Build frameworks for feature computation, storage and access on online, offline and streaming cases
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Build applications with nonSQL storages and cache systems
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Build pipelines offline and streaming related to generative AI needs in Uber
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Collaborate with modeling teams to advance feature engineering at Uber
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Basic Qualifications
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Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related fieldAND5+ years full-time Software Engineering work experience,WHICH INCLUDES3-year total technical software engineering experience in one or more of the following areas:- Programming language (Java, Python, or Go)
Preferred Qualifications:
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Realtime and streaming data pipelines.
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Large nonSQL databases, vector DBs, cache systems.
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Deep experience in scaling modeling, feature engineering, Ray or Spark.
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Py Torch or JAX experience
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For Seattle, WA-based roles: The base salary range for this role is USD**$202,000 per year**
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USD**$224,000 per year**.
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For Sunnyvale, CA-based roles: The base salary range for this role is USD**$202,000 per year**
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USD**$224,000 per year**.
For all US locations, 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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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
Mid/L4
Mid/L4 · Data Analyst
3 reports
$209,300
total / year
Base
$161,000
Stock
-
Bonus
-
$203,580
$209,300
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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