Sr Software Engineer - Matching Platform

About the Team

The Matching Platform team sits at the very heart of Uber's global real-time marketplace. Every second, millions of riders and drivers around the world rely on our platform to connect seamlessly, reliably, and fairly.

Matching is one of the most intellectually challenging and mission-critical engineering domains at Uber. We operate at the intersection of high-throughput distributed systems, dynamic geospatial routing, combinatorial optimization, and production machine learning infrastructure. As a Senior Software Engineer, you will design the foundation for our next-generation dispatch and matching platform, directly influencing marketplace efficiency, driver earnings, and rider experience at massive global scale.

About the Role

In this role, you will take ownership of the architecture, scalability, and operational excellence of Uber's core dispatching systems. You will collaborate across product, infrastructure, data science, and applied machine learning teams to evolve a low-latency platform that balances real-time marketplace dynamics under stringent P99 SLA targets.

What You'll Do

• Design and Scale Real-Time Systems: Architect distributed, fault-tolerant services capable of processing millions of ride requests per second with single-digit millisecond latency.
• Modularize the Platform: Build extensible, domain-driven abstractions and APIs that allow product teams to launch new marketplace innovations without complex architectural rewrites.
• Bridge Systems and ML: Integrate production machine learning models and real-time inference pipelines into the critical serving path for dynamic pricing, ETA estimation, and batch matching.
• Optimize Marketplace Health: Collaborate with algorithms and economics teams to implement solutions that balance efficiency, network liquidity, driver fairness, and rider reliability.
• Drive Engineering Excellence: Lead performance profiling, capacity planning, and chaos testing across high-traffic production environments, raising the bar for system resilience and team best practices.

Basic Qualifications

• 5+ years of professional software engineering experience building production systems.
• Strong proficiency in at least one modern backend programming language (e.g., Go, Java, C++, or Python).
• Deep understanding of computer science fundamentals: data structures, algorithms, concurrency, and distributed system design.
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent professional experience).

Preferred Qualifications

• Distributed Systems at Scale: Proven track record designing, building, and operating high-throughput, low-latency microservices and event-driven architectures.
• Data & Storage Layer Expertise: Deep hands-on experience with distributed databases, caching layers, and storage engines (e.g., Cassandra, MySQL, Redis, Apache Kafka, or Apache Flink).
• ML Serving & Inference: Experience integrating real-time ML inference, feature pipelines, or experimentation platforms into mission-critical production services.
• Performance Engineering: Expertise in distributed profiling, bottleneck identification, memory management, and optimizing P99 latencies under heavy load.
• Collaboration & Technical Leadership: Strong communication skills with a history of mentoring engineers and driving cross-functional technical initiatives from concept to production.

Responsibilities

For San Francisco, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD $202,000 per year - 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.