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.
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.
Performance Fundamentals and Troubleshooting
Core skills for identifying, diagnosing, and resolving general performance problems across applications and systems. Topics include establishing baselines and metrics, using monitoring and profiling tools to determine whether issues are CPU bound, memory bound, input output bound, or network bound, and applying systematic troubleshooting workflows. Candidates should be able to prioritize fixes, recommend temporary mitigations and long term solutions, and explain when to escalate to specialists. This canonical topic covers general performance awareness, common diagnostic tools, and basic remediation approaches for slow systems and resource exhaustion.
Performance Strategy and Resource Efficiency
High level strategy for balancing performance, resource constraints, and cost. Topics include trade off analysis, when to optimize versus accept costs, algorithm and data structure selection under resource constraints, power and energy trade offs, memory and storage budgets, and cost aware performance design. Candidates should discuss prioritization, measurement driven decision making, and resource efficient system design.
Game Engine Architecture
Comprehensive knowledge of game engine structure and runtime behavior, covering the game loop and frame execution model, scene and asset management, entity component system design and object composition patterns, lifecycle and update semantics including fixed update versus variable update timing, prefab and instancing systems, scripting system integration and interop, memory management and object pooling strategies, resource loading and streaming, plugin and extension mechanisms, engine source code exploration and customization, profiling and performance optimization across CPU, GPU, and physics, determinism and networking considerations, and best practices for structuring projects for scalability and maintainability. Includes both engine specific details such as Unity lifecycle methods and prefab workflows and general engine architecture concepts applicable to other engines.
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.
Performance Architecture for Cross Platform Games
Design systems that perform well on diverse hardware: mobile (iOS, Android), console (PS5, Xbox Series X), PC, and web. Discuss how you'd architect for different performance budgets: 60 FPS on console, 30 FPS on mobile, 144 FPS on PC. Consider memory constraints on mobile and web. Discuss level of detail systems, draw call optimization, memory management. Design for profiling and optimization from the ground up.
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.
Systems and Graphics Optimization Techniques
Covers practical optimization techniques commonly used in systems engineering and real time graphics. Topics include object pooling, level of detail systems, frustum culling, spatial partitioning structures such as quadtrees and octrees, batching and texture atlasing, reducing shader complexity and when to use shaders instead of scripted effects, GPU instancing, texture compression strategies, and memory optimization. Emphasizes trade offs between micro optimization and architectural change, profiling to find hotspots, and understanding the law of diminishing returns when applying optimization effort.