Staff Software Engineer, Demand Bidder, Ad Serving Platform

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Skills you'll use

What you'll do

• Design, build, and operate core components of a large-scale, real-time distributed decisioning system with strict latency requirements
• Take end-to-end ownership of specific subsystems: architecture, implementation, testing, deployment, on-call, and long-term evolution
• Build a working mental model of a highly distributed, complex production system, and make high-leverage contributions
• Identify opportunities to continuously evolve the system and tooling
• Partner with engineers and applied scientists working at the intersection of distributed systems and statistical/ML-driven decision-making
• Diagnose and resolve subtle, often timing- or statistics-related issues in a highly concurrent, multi-region distributed system
• Mentor other engineers and raise the bar on engineering rigor, testing discipline, and operational excellence across the team
• Communicate clearly with both technical and non-technical stakeholders, and drive alignment across engineering, product, and business partners

Who can apply

• 10+ years of experience designing, building, and operating large-scale, low-latency distributed systems — ideally including at least one system you built substantially from the ground up
• A demonstrated track record of owning complex systems end-to-end: design, build, operate, on-call, and iterate based on production feedback
• Proven ability to ramp quickly into unfamiliar, complex, large codebases and become productive fast — you enjoy the “figure it out” part of the job as much as the “build it” part
• Experience evolving and refactoring live, business-critical systems without compromising uptime or correctness
• Strong command of Java, with a solid foundation in algorithms, data structures, concurrency/multi-threading, and performance optimization
• Practical experience with distributed caching, high-throughput messaging/streaming systems, and SQL/NoSQL datastores
• Comfort operating in cloud infrastructure (GCP or AWS) at scale
• Exposure to applying statistical, control-theory, or ML-driven techniques within production decision-making systems is a strong plus

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