Backend Engineering & Performance Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Performance Engineering and Cost Optimization
Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.
Complexity Analysis and Performance Modeling
Analyze algorithmic and system complexity including time and space complexity in asymptotic terms and real world performance modeling. Candidates should be fluent with Big O, Big Theta, and Big Omega notation and common complexity classes, and able to reason about average case versus worst case and trade offs between different algorithmic approaches. Extend algorithmic analysis into system performance considerations: estimate execution time, memory usage, I O and network costs, cache behavior, instruction and cycle counts, and power or latency budgets. Include methods for profiling, benchmarking, modeling throughput and latency, and translating asymptotic complexity into practical performance expectations for real systems.
Cryptographic Performance and Scalability
Designing cryptographic components and systems for performance and scale while preserving security. Topics include optimizing throughput and latency, selecting algorithms and parameters with performance in mind, hardware acceleration and processor instruction support for cryptographic primitives, batching and parallelization strategies, trade offs between key sizes and performance, handshake and session optimization for protocols, resource constraints for embedded and mobile devices, benchmarking and profiling, and planning for operational scale including hardware security module capacity and key management throughput.
Performance and Scalability in Cryptography
Candidates should be able to reason about performance and scalability trade offs of cryptographic choices in real systems and quantify their impact. Topics include algorithmic complexity and constant factors, latency and throughput trade offs, differences in cost between symmetric and asymmetric operations, the impact of key sizes and parameter choices, memory and battery constraints on client devices, batching and parallelization strategies, and options for hardware acceleration or offload. Practical skills include benchmarking and profiling cryptographic hotspots, choosing modes that balance speed and security, and designing systems that meet service level objectives while preserving required cryptographic guarantees.