Staff Software Engineer

Uber

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Sunnyvale, CASan Francisco, CA
Posted: 3 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $197k

$128k $282k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $172,500–$221,750.

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 109 open roles on FindRole.

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At a glance

TL;DR · Staff Software Engineer

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Staff Software Engineer - AI Infra - Training will serve as a technical anchor for the foundational systems supporting global applications. In this role, you will design and build end-to-end systems for the machine learning training stack, specifically addressing scaling challenges for very large datasets and models. Your daily responsibilities include providing deep technical mentorship, creating tools and frameworks to increase engineering velocity, and using statistical analysis to diagnose performance regressions. You must demonstrate proficiency in programming languages such as Go, C/C++, Java, Python, or JavaScript, alongside expertise in distributed training frameworks, LLMs, and GPU optimizations. You will solve complex infrastructure problems across global data centers and multiple cloud providers, making critical architectural tradeoffs to reduce technical debt and optimize resource utilization.

What does a Software Engineer earn in California?

Median $214500 from 1126 postings across 74 companies.

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Skills

Distributed Training LLMs GPU Optimization Go C++ Java Python JavaScript Distributed Computing ML Training Stack Statistical Analysis Productionization

What you'll do

Design and build end-to-end systems for the ML training stack to solve large-scale dataset and model challenges.
Provide deep technical mentorship to ensure architecture and implementations are robust, extensible, and secure.
Use statistical analysis to diagnose performance regressions and optimize resource utilization across complex infrastructures.
Create tools and frameworks that increase the velocity and quality of internal engineering teams.
Lead engineers by reviewing code and designs while fostering a culture of continuous learning.
Drive large-scale projects by aligning technical investments with business value across global teams.
Identify architectural gaps and act as a multiplier to reduce technical debt across the organization.

What we're looking for

8+ years of professional experience building scalable, fault-tolerant products or platforms.
Proficiency in one or more programming languages such as Go, C/C++, Java, Python, or JavaScript.
Proficiency in state-of-the-art deep learning technologies, including distributed training frameworks, LLMs, and GPU optimizations.
Proven experience leading large-scale engineering projects from inception to global productionization.
Expertise in making complex engineering and business tradeoffs, including "buy vs. build" and "incremental vs. rewrite" decisions.
Experience identifying architectural gaps and reducing technical debt across an organization.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
Advanced degree in distributed computing, system performance, or machine learning (preferred).

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