Requirements

8+ years of experience as a software engineer working on production backend systems
Strong hands-on expertise in C++ or Rust in real-world, high-load services
Experience building and operating high-load, low-latency user-facing systems handling thousands of RPS under strict latency constraints
Deep understanding of performance at a systems level — including CPU, memory, networking, and data access
Experience operating your own code in production: deployment, debugging incidents, and rolling back changes when necessary
Ability to think end-to-end about request flows rather than isolated components
Skill in balancing correctness, latency, and development velocity, making pragmatic tradeoffs as needed
Effective collaboration and clear communication across engineering, ML, and product teams

Nice to Haves

Experience with DBMS internals (open source or SaaS) and cloud infrastructure
Background in high-load web applications or large-scale APIs
Experience with performance-critical systems such as trading platforms or real-time data pipelines
Knowledge of low-level performance tuning and hardware-level optimization
Open-source contributions or active involvement in the engineering community
Participation in competitive programming or CTFs
Completion of SHAD or similar advanced technical programs
Giving conference talks or publishing technical papers

Designing, implementing, and operating core runtime services for serving search queries at scale
Building and optimizing request flows including query processing, retrieval orchestration, and response assembly under strict latency budgets
Developing systems that maintain performance and predictability under high load
Optimizing CPU, memory, and data access patterns in performance-critical paths
Ensuring reliability, observability, and predictability across production services
Building well-tested systems with clear responsibilities and interaction contracts, adapting to evolving architecture
Defining and implementing observability primitives such as structured logs, metrics, traces, and latency breakdowns
Monitoring throughput, latency, and resource usage; driving improvements in performance and cost efficiency
Collaborating with indexing and ML teams to integrate retrieval and ranking components while keeping ML logic decoupled from core systems
Supporting experimentation and iteration with controlled rollouts and rigorous benchmarking