Anthropic·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

System design round at Anthropic focused entirely on low-level performance engineering. One question, very deep, and they clearly wanted to see if you actually understood the hardware implications of your design choices rather than just reciting theory.

Questions Asked (1)

Design a high-throughput hash table for use inside a tight compute kernel. Walk through your choice of open addressing vs chaining, load factor, probe sequence, and memory layout with an eye toward vectorization and cache behavior. Include how you'd use bitwise ops, prefetching, and alignment to minimize collisions and cache misses, and explain how you'd profile and validate the whole thing.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

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Suggested Approach

Start by clarifying the workload characteristics (key/value sizes, operation mix, latency/throughput targets) to ground your design choices. Then systematically compare open addressing vs chaining, justify a load factor and probe sequence, and detail a cache-friendly, vectorizable memory layout. Finally, explain how you'd use bitwise ops, prefetching, and alignment, and describe a profiling and validation plan.

Pro tip: Emphasize that you'd prototype multiple variants and measure with hardware counters (cache misses, branch mispredictions) rather than relying on intuition—this shows a data-driven, iterative approach that senior engineers value.

1. Clarify requirements and constraints

Ask about key/value sizes, expected load, operation mix (insert/lookup/delete), and performance targets (throughput, latency, memory). This ensures your design is tailored to the actual use case.

2. Choose collision resolution and load factor

Compare open addressing (e.g., linear probing, Robin Hood) vs chaining. Justify open addressing for cache efficiency and vectorization, and pick a load factor (e.g., 0.5–0.7) balancing memory and probe length.

3. Design memory layout and probe sequence

Propose a flat array of slots with aligned structs, using bitwise ops for fast modulo (power-of-two capacity) and a probe sequence like quadratic or double hashing. Discuss SIMD-friendly layouts (e.g., SoA) for batch operations.

4. Optimize with prefetching and alignment

Explain how to use software prefetching (__builtin_prefetch) to hide memory latency during probing, and ensure cache-line alignment to avoid false sharing and maximize bandwidth.

5. Profile and validate

Outline a plan: microbenchmarks with varying load factors, hardware performance counters (perf) to measure cache misses and IPC, and correctness tests (e.g., property-based) to ensure no collisions or data races.

Key Points to Mention

Open addressing vs chaining: cache locality, memory overhead, and suitability for vectorization.
Load factor trade-off: lower load reduces probes but increases memory; higher load saves memory but increases collisions.
Probe sequence: linear probing for cache friendliness, quadratic/double hashing to reduce clustering.
Bitwise operations: using power-of-two capacity and bitmask for fast modulo, and hash mixing with XOR/shift.
Prefetching: issuing prefetch instructions for upcoming probe addresses to hide latency.
Alignment: aligning slots to cache lines to avoid false sharing and enable SIMD loads.
Profiling: using perf counters, microbenchmarks, and correctness validation.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.