Staff Software Engineer - Traffic Machine Learning

Staff Software Engineer - Traffic Machine Learning Department: Engineering Team: Machine Learning Location: Amsterdam, Netherlands Type: Full-Time **About the Role** Engineering at Uber means building for real-world impact under real-world constraints. The Traffic team owns the real-time heartbeat of our platform—processing road data, incidents, closures, and weather that dictate how every Uber trip is routed and priced. This isn’t a place for theoretical exercises; you will be building the systems that millions of people rely on to get where they’re going in the real world. The work is high-stakes and technically complex. You will be dealing with massive scale and the inherent messiness of real-world data, where performance and safety are inseparable. We need someone who thinks in systems, stays calm when production latency spikes, and has the grit to navigate technical debt while building for the future. If you thrive in high-autonomy environments and want to own the technical vision for one of Uber’s most critical domains, this is where you’ll grow. If you prefer a predictable playbook or a slow pace, this likely isn't the right fit. **What You’ll Do** - Design, build, and maintain data pipelines that process real-time road data at a global scale, where every millisecond of latency impacts millions of ETAs. - Lead technically complex initiatives, such as re-architecting our incident and closure detection systems to improve accuracy and reaction time. - Solve messy, high-impact problems—like optimizing the interface between traffic and routing—often without a clear starting point or obvious solution. - Navigate the trade-offs between short-term tactical fixes and long-term architectural stability while keeping our Maps ecosystem running smoothly. - Own your work end-to-end, from drafting the multi-year technical vision for traffic domains to debugging production issues when the stakes are high. - Collaborate cross-functionally with Data Scientists, Product Managers, and Engineering peers to translate complex business needs into robust, scalable software. - Champion engineering best practices like code he