About the role and team
Engineering at Uber means building for real-world impact under real-world constraints, where software decisions directly move people, goods, and local economies globally. As a Staff Software Engineer, you will define and drive broad technical strategies across core systems, bridging high-level business vision with robust, scalable engineering architectures. You will lead complex technical initiatives end-to-end, serving as a technical multiplier across multiple organizations, setting high standards for software quality, resilience, and operational excellence.
The work at this level is inherently ambiguous, high-stakes, and fast-paced, requiring you to make critical architectural trade-offs with imperfect information. You will navigate legacy complexity, shifting priorities, and massive distributed scale while empowering engineers, aligning cross-functional partners, and unblocking critical path systems. If you are energized by solving messy, real-world problems, taking end-to-end ownership, and raising the engineering bar across an entire platform—this is where you will make your mark.
What You’ll Do
Technical Strategy: Define the 1-2 year technical vision for core surfaces, preparing backend systems for 10x growth.
Architect for Scale: Lead the implementation of mission-critical distributed systems for real-time personalization.
Mentorship & Influence: Coach senior and lead engineers while running high-level design reviews to maintain excellence.
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Basic Qualifications
Experience: 10+ years of experience with a track record of leading large-scale initiatives in a Staff capacity.
Technical Depth: Exceptional expertise in massive concurrency and low-latency distributed systems.
Education: BS/MS/PhD in Computer Science or related industry experience.
Preferred Qualifications
Advanced Degree: Master’s or PhD in Computer Science or a related field.
Strategic Influence: Experience defining 1-2 year technical visions that align engineering goals with business outcomes.
Domain Expertise: Deep experience in integrating AI/ML models into high-throughput backend production surfaces.
Open Source: Contributions to open-source distributed systems or infrastructure projects (e.g., Go, Kafka, M3).
