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职位Uber

Staff Software Engineer : Storage, Search, & Data Platforms

Uber

Staff Software Engineer : Storage, Search, & Data Platforms

Uber

San Francisco, CA; Sunnyvale, CA

·

On-site

·

Full-time

·

1w ago

The Storage, Search, and Data (SSD) group is the custodian of Uber's digital intelligence. We provide the unified data fabric-from transactional persistence to real-time discovery-that powers every ride, delivery, and autonomous decision made across the planet.

As a Staff Engineer, you will operate as a primary technical architect for the SSD organization. You will be responsible for the technical vision that bridges our on-premise footprint with a Modern, Cloud-Native Data Ecosystem. This role requires a rare blend of deep distributed systems expertise, leading from the front, and the strategic influence to align multiple platform teams toward a unified "Data-to-AI" future.

What You Will Lead

  • Strategic Evolution: Define and execute the multi-year roadmap to transition Uber from "Data Storage" to a Cloud-Native Data Provider, solving for cross-region latency, global metadata consistency, and exabyte-scale cost efficiency.
  • The AI-Data Convergence: Partner with Uber's AI/ML leadership to architect the "Data-to-GPU" pipeline. You will design the one-stop storage APIs that allow researchers to leverage high-performance data access across multi-cloud regions and vendors seamlessly.
  • Platform Innovation: Drive the next generation of our core engines: Docstore (NoSQL), Vitess (Sharded MySQL), Apache Pinot (Real-time Analytics), and Open Search (Discovery).
  • You will represent Uber in the global community as a leader in key open source technologies including Apache, Hudi, Iceberg and many others

---- Basic Qualifications ----

  • Industry Leadership: 12+ years of software engineering experience, with a proven history of designing and operating massive-scale distributed data systems.
  • Systems Mastery: Elite engineering skills in Go, Java, C++, or Rust. You are comfortable deep-diving into database internals, kernel-level optimizations, and complex distributed consensus protocols.
  • Strategic Execution: Proven experience leading technical strategy across multiple teams or organizations, turning high-level business goals into concrete technical realities.
  • Operational Excellence: Extensive experience managing Tier-0, mission-critical systems with 99.99% availability and global blast-radius constraints.

---- Preferred Qualifications ----

  • Advanced Academic Background: MS / PhD in Computer Science (or equivalent experience) with a focus on Distributed Systems, Database Internals, or Large-Scale AI Infra.

  • Open Source Authority: You are a Maintainer or PMC member of an industry-defining project (e.g., Apache Pinot, Ray, Iceberg, Lance, or Gravitino).

  • AI Infrastructure Depth: Deep understanding of modern AI hardware-software interfaces, including GPU memory management, high-bandwidth networking (RDMA/NCCL), and training data pipelines.

  • Cloud-Native Visionary: Extensive experience leveraging S3/GCS/OCI to build disaggregated storage-compute architectures that eliminate "stranded" resources and minimize cross-region costs.

  • Mentorship at Scale: A track record of growing all levels of engineering talent, fostering a culture of technical excellence and scale.

  • For San Francisco, CA-based roles: The base salary range for this role is USD**$232,000 per year**

  • USD**$258,000 per year**.

  • For Seattle, WA-based roles: The base salary range for this role is USD**$232,000 per year**

  • USD**$258,000 per year**.

  • For Sunnyvale, CA-based roles: The base salary range for this role is USD**$232,000 per year**

  • USD**$258,000 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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关于Uber

Uber

Uber

Public

Uber develops, markets, and operates a ride-sharing mobile application that allows consumers to submit a trip request.

10,001+

员工数

San Francisco

总部位置

$120B

企业估值

评价

3.7

10条评价

工作生活平衡

3.2

薪酬

4.0

企业文化

4.1

职业发展

3.4

管理层

2.8

68%

推荐给朋友

优点

Good compensation and pay

Flexible hours and schedule

Great team culture and colleagues

缺点

Long hours and tight deadlines

High pressure and stressful environment

Poor management and lack of support

薪资范围

15,354个数据点

Mid/L4

Mid/L4 · Data Analyst

3份报告

$209,300

年薪总额

基本工资

$161,000

股票

-

奖金

-

$203,580

$209,300

面试经验

5次面试

难度

3.0

/ 5

时长

14-28周

录用率

40%

体验

正面 80%

中性 20%

负面 0%

面试流程

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

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Case Study

Technical Knowledge