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Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jul 2026Remote

Summary

Anthropic software engineering interview with a coding question centered on implementing an LRU-based memoization helper from scratch, plus a persistence/recovery follow-up that caught me a bit flat-footed.

Questions Asked (2)

Q1

Implement an LRU memoization helper class with a configurable capacity. It should include a method to generate a deterministic cache key from a function and its arguments (handling both positional and keyword args, with keyword arg order not mattering), and a method that returns a cached result if available or computes, caches, and returns it otherwise.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I knew the LRU eviction part pretty well, ordered dict does most of the heavy lifting there.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases (e.g., unhashable args, capacity <= 0, thread safety). Then design the key generation to canonicalize arguments (e.g., sort kwargs, handle unhashables) and implement an LRU cache using an OrderedDict or doubly linked list + hash map. Finally, discuss trade-offs and potential optimizations.

Pro tip: Mention that you would use functools.lru_cache as a reference but implement your own to handle unhashable arguments and custom key generation, showing deeper understanding. Also, proactively discuss thread safety and whether the cache should be thread-local or global.

1. Clarify Requirements and Edge Cases

Ask about expected argument types (hashable vs unhashable), capacity behavior (e.g., zero or negative), thread safety, and whether the cache should be per-function or global. This shows thoroughness and avoids assumptions.

2. Design Deterministic Key Generation

Create a key from the function and its arguments: use a tuple of (func, args, sorted kwargs items). For unhashable args, either raise an error or convert to a hashable representation (e.g., repr or custom serialization).

3. Implement LRU Cache Mechanism

Use an OrderedDict to store key-value pairs and track access order. On get, move the key to the end; on put, add to the end and evict the first item if capacity is exceeded. Alternatively, implement a doubly linked list + hash map for O(1) operations.

4. Integrate Key Generation and Caching

In the memoize method, generate the key, check the cache, and if miss, compute the result, store it, and return. Ensure thread safety if required (e.g., using locks).

5. Discuss Trade-offs and Extensions

Talk about time/space complexity, eviction policy alternatives (e.g., LFU), handling of unhashable arguments, and potential optimizations like using a weak reference for the function. Also mention testing strategies.

Key Points to Mention

  • Deterministic key generation: sorting keyword arguments and handling unhashable types (e.g., lists, dicts) via conversion to hashable tuples or frozensets.
  • LRU implementation using OrderedDict or a combination of hash map and doubly linked list for O(1) get and put operations.
  • Capacity management: eviction of least recently used item when capacity is exceeded, and behavior when capacity is zero or negative.
  • Thread safety considerations: using locks or thread-local caches, and the impact on performance.
  • Trade-offs: memory overhead vs. speed, cache invalidation strategies, and when to use built-in functools.lru_cache vs. custom implementation.
  • Edge cases: recursive functions, methods (self argument), and functions with default arguments.

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

Q2

Follow-up: if the process crashes and the in-memory cache is lost, how would you persist the cache to disk so it can be restored correctly after a restart? What data would you write, when would you write it, and how would recovery work?

System DesignTechnical Trade-offs
Author's notes

This is where I started rambling.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the cache's role and consistency requirements, then propose a persistence strategy that balances durability and performance, such as periodic snapshots plus a write-ahead log. Explain what data to persist (key-value pairs, metadata, versioning), when to write (on mutation, periodically, or on shutdown), and how recovery would replay the log and load the snapshot to restore the cache.

Pro tip: Emphasize that the cache is a performance optimization, not the source of truth—so persistence should be best-effort and never block the main request path; consider using a separate thread or process for disk writes.

1. Clarify requirements and constraints

Ask about acceptable data loss (RPO), recovery time (RTO), cache size, and write throughput to determine if persistence is even necessary or if a cold cache is acceptable.

2. Choose a persistence strategy

Select between snapshotting (periodic full dumps), write-ahead logging (append-only log of mutations), or a hybrid approach, considering trade-offs in performance, disk usage, and recovery complexity.

3. Define what data to persist

Persist key-value pairs, expiration timestamps, version numbers, and any metadata needed to reconstruct the cache state consistently; consider serialization format (e.g., JSON, Protobuf).

4. Determine when to write

Write on every mutation (synchronous or asynchronous), periodically (e.g., every N seconds or M operations), or on graceful shutdown; balance durability against performance overhead.

5. Design recovery process

On restart, load the latest snapshot, then replay the write-ahead log to apply recent mutations, ensuring idempotency and handling partial writes or corruption.

Key Points to Mention

  • Write-ahead logging (WAL) for durability and crash recovery
  • Periodic snapshots to bound recovery time and log size
  • Asynchronous I/O to avoid blocking the main request path
  • Data serialization format and schema evolution
  • Idempotent recovery and handling of partial writes
  • Cache invalidation and consistency with the source of truth

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