715 open roles
Staff Software Engineer - Engineer
$232,000
Against the Sunnyvale typical range
Job description
Staff Software Engineer
- AI Infra
- Training Description About the role and team Leading at Uber means setting direction in motion, not waiting for perfect conditions. As a Staff Software Engineer, you will be a technical anchor for the foundational systems that move the world - from building the next generation of orchestration platforms to architecting the stacks that power our global applications. Our mission is to make transportation as reliable as running water, and we need technical powerhouses to build the dependable, high-performance foundation that supports that vision. This is a role for a proven, large-scale systems thinker who thrives in high-stakes environments. You will navigate the "messy" reality of complex infrastructures spanning global data centers and multiple cloud providers, supporting a diverse variety of workloads. You’ll need to make smart decisions with imperfect information and own the outcomes of global architectural choices. If you lead with clarity, build with heart, and are motivated by the challenge of peak efficiency at unparalleled scale - this is where you’ll make your mark. What you’ll do Design and build end-to-end systems for Uber’s ML training stack. Solve the scaling challenges on very large datasets and models. Provide deep technical mentorship across the stack, ensuring architecture and implementations are robust, extensible, and secure. Build innovative solutions that leverage statistical analysis to diagnose performance regressions and optimize resource utilization across complex infrastructures. Create best-in-class tools and frameworks that increase the velocity and quality of hundreds of internal engineering teams. Lead and mentor engineers, reviewing code and designs while fostering a culture of continuous learning and humility. Work cross-functionally with Product, Data Science, and Operations across the globe to drive large-scale projects and align technical investments with business value.
Basic Qualifications
8+ years of professional experience building scalable, fault-tolerant products or platforms, with a track record of solving strategically important problems. Proficiency in one or more programming languages (e.g., Go, C/C++, Java, Python, or JavaScript) and the ability to define organization-wide standards. Proficiency in state of the art deep learning technologies, such as distributed training frameworks, LLMs, GPU optimizations, etc.
Proven experience leading large-scale engineering projects through the entire lifecycle from inception to global productionization. Expertise in making complex engineering/business tradeoffs, managing the implications of "buy vs. build" or "incremental vs. rewrite" decisions. Experience identifying architectural gaps and acting as a multiplier to reduce technical debt across the organization.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
Preferred Qualifications
Advanced degree (Master’s or PhD) with a focus on distributed computing, system performance, or machine learning. Experience contributing to open-source projects or leading industry-level technical discussions. Ability to anticipate future business needs and develop extensible, modular designs that meet long-term scalability targets.
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.
Description copied from Uber's careers page. Read the full posting before you apply.
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