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.
Mobile Performance and Energy Optimization
Comprehensive engineering and operational practices for diagnosing, profiling, and optimizing mobile application performance and device energy consumption at feature and system scale. Candidates should be able to explain strategies to reduce application startup time, minimize main thread work to keep the user interface responsive, and stabilize rendering at target frames per second such as sixty frames per second and one hundred and twenty frames per second to avoid application not responding situations. Core topics include memory management and leak prevention, allocation analysis, preventing crashes and responsiveness regressions, efficient rendering of large feeds, complex gesture and input handling, and efficient handling of large media such as photos and video. Common techniques include lazy loading, request batching, image resizing and compression, caching and batching strategies, offline first synchronization, and efficient background processing and scheduling to limit energy impact. Energy and battery focused optimizations include minimizing sensor usage and location service use when unnecessary, geofencing best practices, network and radio optimizations to reduce radio wake ups, preferring push driven updates over polling where appropriate, and designing background tasks to be energy aware. Candidates should demonstrate familiarity with profiling and instrumentation tools and workflows for mobile platforms, interpreting profiler output to identify central processing unit and memory bottlenecks, measuring and quantifying latency and energy impact, designing architectural and code changes to prevent regressions, reasoning about trade offs between native and cross platform implementations, and defining user perceived performance and energy metrics with continuous monitoring and tests to quantify improvements.
Performance Profiling and Optimization
Comprehensive skills and methodology for profiling, diagnosing, and optimizing runtime performance across services, applications, and platforms. Involves measuring baseline performance using monitoring and profiling tools, capturing central processing unit, memory, input output, and network metrics, and interpreting flame graphs and execution traces to find hotspots. Requires a reproducible measure first approach to isolate root causes, distinguish central processing unit time from graphical processing unit time, and separate application bottlenecks from system level issues. Covers platform specific profilers and techniques such as frame time budgeting for interactive applications, synthetic benchmarks and production trace replay, and instrumentation with metrics, logs, and distributed traces. Candidates should be familiar with common root causes including lock contention, garbage collection pauses, disk saturation, cache misses, and inefficient algorithms, and be able to prioritize changes by expected impact. Optimization techniques included are algorithmic improvements, parallelization and concurrency control, memory management and allocation strategies, caching and batching, hardware acceleration, and focused micro optimizations. Also includes validating improvements through before and after measurements, regression and degradation analysis, reasoning about trade offs between performance, maintainability, and complexity, and creating reproducible profiling hooks and tests.
Caching Strategies and In Memory Storage
Understanding caching mechanisms (HTTP caching, application-level caching with Redis/Memcached). Cache invalidation strategies, TTL, and when to cache. Performance implications.
Technical Performance Awareness
Addresses awareness of software and system performance considerations: identifying bottlenecks, profiling tools, time and space complexity trade offs, efficient resource usage, platform specific constraints such as frame rate and battery for mobile, and best practices for optimization. Candidates should be able to explain profiling workflows, common performance pitfalls, and how to prioritize performance improvements without premature optimization.
Platform Specific Optimization and Constraints
Understand optimization requirements for different platforms: Mobile (iOS, Android) with battery, thermal, and memory constraints; Console (PS5, Xbox Series X) with specific hardware; PC with varying hardware; Web with bandwidth and performance constraints. Discuss target frame rates: 60 FPS console/PC, 30-60 FPS mobile. Discuss how you'd scale game quality for different platforms: resolution, particle count, draw distance, physics precision. Understand platform-specific tools: Xcode profiler for iOS, Android Profiler for Android, console profiling tools.
Garbage Collected Memory Management
Covers memory management in managed runtimes that use garbage collection. Topics include the memory model distinguishing value types and reference types, stack and heap allocation patterns, how common garbage collection algorithms work and their runtime impacts such as pause times and allocation throughput, causes of allocation pressure, and strategies to reduce garbage collection overhead. Practical techniques include avoiding boxing, reusing and preallocating collections, using value types or structs for small frequently instantiated data, object pooling, and data oriented design trade offs versus object oriented design for performance. Candidates should also know profiling tools, memory budgeting for constrained platforms such as mobile or game consoles, and platform specific considerations for engines like Unity.
Optimization and Technical Trade Offs
Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.
Advanced Linux Performance and Services
Advanced administration focused on service lifecycle, process management, and system performance. Topics include deep systemd service management and unit file authoring, dependency ordering and service recovery, process lifecycle and signal handling, cgroups and resource controls, tuning kernel parameters, diagnosing CPU and memory pressure, understanding page cache and swap behavior, out of memory scenarios, I O performance analysis, interpreting load average, and using performance and sampling tools such as top, htop, pidstat, iostat, vmstat, sar, and perf for identifying bottlenecks and implementing mitigations.