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Tests ability to represent intent and incorporate it into planning or prediction for safe driving.
Tests ability to design safe degraded-mode behavior and robust state handling in autonomy.
Tests system design trade-offs for capturing telemetry at scale without impacting real-time autonomy.
Tests ability to connect ML components to classical planning stacks with clear interfaces and failure handling.
Tests practical profiling skills and ability to reduce memory overhead and improve runtime stability.
Tests understanding of determinism, scheduling, and operational complexity for real-time autonomy.
Tests ability to design real-time filtering algorithms that balance latency, noise reduction, and stability.
Tests ability to design objective functions that trade off competing motion quality goals.
Tests engineering judgment for improving code quality, modularity, and test coverage in C++ systems.
Tests practical latency reduction across networking, buffering, and processing pipelines in autonomy.
Tests judgment in balancing long-term quality with short-term delivery in engineering execution.
Tests leadership in technical decision-making and coordination across dependencies in autonomy development.
Tests understanding of degraded-mode behavior and safe recovery strategies.
Tests prioritization under constraints and ability to align engineering work with safety-critical goals.
Tests understanding of interface mismatch, reliability, and validation when combining ML and classical planning.
Tests debugging strategy, concurrency understanding, and use of tools to reproduce and fix nondeterminism.
Tests build tooling experience that supports fast, reliable iteration across many components and teams.
Tests concurrency control, scheduling awareness, and strategies to meet real-time constraints.
Tests ability to build correct, high-performance concurrent data structures for real-time robotics pipelines.
Tests safety validation approach for ML policies in safety-critical autonomous driving.
25 total questions