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
Understanding of performance at a systems level — CPU, memory, networking, and data access
Experience deploying, debugging, and rolling back code in production environments
Ability to think end-to-end about request flows rather than isolated components
Ability to balance correctness, latency, and development velocity with pragmatic tradeoffs
Effective collaboration and clear communication skills across engineering, ML, and product teams

Nice to Haves

Experience with DBMS internals (open source or SaaS) and cloud infrastructure
Experience with high-load web applications or large-scale APIs
Experience with performance-critical systems such as trading platforms or real-time data pipelines
Low-level performance tuning and hardware-level optimization skills
Open-source contributions or active involvement in the engineering community
Participation in competitive programming or Capture The Flag (CTF) events
Completion of advanced technical programs like SHAD
Experience 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 within 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, while maintaining flexibility as architecture evolves
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 system internals
Supporting experimentation and iteration through controlled rollouts and rigorous benchmarking