Software Engineering Practices Topics
Covers industry-standard practices for building maintainable, high-quality software, including code quality, maintainability, documentation, and effective technical communication within engineering teams.
Embedded System Architecture and Design
Principles for decomposing firmware into logical components and layers to support robustness and extensibility. Cover hardware abstraction layers and device drivers, separation of platform specific code and application logic, bootloader and update path design, communication stacks and middleware, and clear interface contracts for subsystems. Discuss memory layout implications, power management strategies, testability and observability, and support for multiple hardware variants and protocol versioning. Include considerations for deployment and over the air updates, backward compatibility, and incremental rollout strategies.
Scalability and Maintenance in Embedded Systems
Approaches for keeping firmware maintainable and scalable as product complexity and team size grow. Topics include modular code organization, abstraction and separation of concerns, configuration and feature gating for multiple hardware variants, protocol versioning and backward compatibility, build and test automation including hardware in the loop testing, continuous integration for firmware, and clear documentation and ownership. Discuss strategies for safe refactoring, deprecation policies, and how to balance short term delivery with long term code health. Include considerations for monitoring, remote debugging, and long term operational support.
Code Review and Optimization Practices
Practices and techniques for reviewing embedded code and implementing performance and memory optimizations while maintaining correctness and maintainability. Topics include establishing code review checklists that focus on concurrency interrupt safety hardware abstraction and memory usage, using static analysis and linters, building effective unit and integration tests, profiling and instrumentation to find hotspots, choosing algorithmic and data structure optimizations, safe use of compiler optimization flags and pragmas, trade offs between readability and micro optimizations, measuring improvements with benchmarks and regression tests, and documenting rationale and risk for changes. Candidates should be able to give concrete examples of reviews they performed or led, describe tools and metrics used, and explain the impact of optimizations.
Ownership and Quality
Assessment of a candidate's willingness to take responsibility for outcomes and their practices for ensuring high quality deliverables. Candidates should be able to describe owning features or incidents end to end, setting and enforcing definitions of done, improving reliability through testing strategies and code review, reducing or managing technical debt, and following through on production issues. Interviewers may probe how the candidate balances shipping with quality, how they escalate and communicate risk, and concrete examples of improving system correctness and observability.