Tools, Frameworks & Implementation Proficiency Topics
Practical proficiency with industry-standard tools and frameworks including project management (Jira, Azure DevOps), productivity tools (Excel, spreadsheet analysis), development tools and environments, and framework setup. Focuses on hands-on tool expertise, configuration, best practices, and optimization rather than conceptual knowledge. Complements technical categories by addressing implementation tooling.
Designer Tools and Content Systems
Design and implementation of tools, editors, and content pipelines that enable designers and artists to create and iterate on gameplay and assets without engine code changes. Topics include editor user experience, safe scripting interfaces, hot reload and live editing, serialization and version control integration, asset import and validation pipelines, tooling for tuning and balancing, automation of content packaging, performance considerations for editor workflows, and support and testing strategies for tools. Candidates should discuss trade offs between exposing functionality to non programmers and maintaining runtime integrity, as well as approaches to make tooling extensible and reliable for large teams.
Apple Frameworks & APIs
Knowledge of Apple native frameworks and APIs for iOS/macOS development, including commonly used frameworks (UIKit, SwiftUI, Foundation, Core Data, Combine, AVFoundation, Core Animation, Core Location, CloudKit, and more), bridging between Swift and Objective-C, memory management with ARC, and platform-specific integration patterns.
Technology Stack and Interests
Covers both the team and product technology choices you will encounter and the candidate's own technical experience and learning interests. Topics include common frameworks and languages used in modern stacks such as React, Vue, Angular, TypeScript and backend platforms, as well as build tools, testing frameworks, deployment tooling, and styling approaches. Candidates should be prepared to explain why certain technologies were chosen, trade offs and migration paths, which parts of the stack they expect to learn on the job, and how their existing skills translate to the company stack. Interviewers also assess genuine interest in the company technologies, learning agility, adaptability to new tools, and practical experience with relevant frameworks, libraries, or patterns. Good answers combine a clear understanding of the team stack, examples of past experience, and a plan for rapid skill acquisition where needed.
Technical Tools and Stack Proficiency
Assessment of a candidates practical proficiency across the technology stack and tools relevant to their role. This includes the ability to list and explain hands on experience with programming languages, frameworks, libraries, cloud platforms, data and machine learning tooling, analytics and visualization tools, and design and prototyping software. Candidates should demonstrate depth not just familiarity by describing specific problems they solved with each tool, trade offs between alternatives, integration points, deployment and operational considerations, and examples of end to end workflows. The description covers developer and data scientist stacks such as Python and C plus plus, machine learning frameworks like TensorFlow and PyTorch, cloud providers such as Amazon Web Services, Google Cloud Platform and Microsoft Azure, as well as design tools and research tools such as Figma and Adobe Creative Suite. Interviewers may probe for evidence of hands on tasks, configuration and troubleshooting, performance or cost trade offs, versioning and collaboration practices, and how the candidate keeps skills current.
Relevant Team and Stack Experience
Demonstrate past experience and domain knowledge that directly map to the team's work and technical stack. This includes familiarity with the specific game genres or platform constraints the team targets such as multiplayer, mobile, competitive, or virtual reality games, and the problems and trade offs those domains introduce. It also covers hands on experience with the team's toolchain and architecture including game engines, middleware, build pipelines, deployment targets, networking models, platform SDKs, and common performance or memory constraints. Candidates should be able to explain concrete examples from their history where they applied relevant technologies or patterns, how they adapted to a new stack, and how their background would accelerate onboarding to the team.
Game Engine and Language Proficiency
Demonstrate hands on experience with commercial and open source game engines such as Unity, Unreal Engine, Godot, and similar platforms, and the programming languages commonly used with them (for example C sharp and C plus plus). Describe specific systems, features, or tools you built inside an engine, such as gameplay systems, rendering or shader work, physics integration, animation systems, editor tooling, asset pipelines, or multiplayer and replication logic. Be prepared to discuss engine workflows, scripting versus native modules, performance optimization and profiling, memory and asset management, build and deployment pipelines for consoles or mobile, and familiarity with engine idioms and debugging tools. Emphasize practical proficiency level, trade offs you made, and examples that show depth of experience rather than only naming technologies.
Game Engine Familiarity
Discuss your experience with game engines like Unity or Unreal Engine. Mention specific features you've used (physics engine, animation systems, UI framework, scripting), any tutorials or documentation you've studied, and projects where you applied these tools. If you lack experience with both major engines, explain which one you're focusing on and why.
Technology Stack Knowledge
Assess a candidate's practical and conceptual understanding of technology stacks, including major programming languages, application frameworks, databases, infrastructure, and supporting tools. Candidates should be able to explain common use cases and trade offs for languages such as Python, Java, Go, Rust, C plus plus, and JavaScript, including differences between compiled and interpreted languages, static and dynamic type systems, and performance characteristics. They should discuss application frameworks and libraries for frontend and backend development, common web stacks, service architectures such as monoliths and microservices, and application programming interfaces. Evaluate understanding of data storage options and trade offs between relational and non relational databases and the role of structured query language. Candidates should be familiar with cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, infrastructure components including containerization and orchestration tools such as Docker and Kubernetes, and development workflows including version control, continuous integration and continuous delivery pipelines, testing frameworks, automation, and infrastructure as code. Assess operational concerns such as logging, monitoring and observability, deployment strategies, scalability, reliability, fault tolerance, security considerations, and common failure modes and mitigations. Interviewers may probe both awareness of specific tools and the candidate's depth of hands on experience, ability to justify technology choices by evaluating trade offs, constraints, and risk, and willingness and ability to learn and evaluate new technologies rather than claiming mastery of everything.
Cross Platform Compatibility and Build Optimization
Understand challenges of building for multiple platforms: different GPU capabilities, different file systems, different input methods, different screen resolutions and aspect ratios. Discuss code structure that supports multiple platforms cleanly using conditional compilation and abstraction layers. Understand build size implications and optimization: asset compression, build format optimization. Discuss testing across platforms and device fragmentation challenges.