Anthropic·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Anthropic software engineer interview that went deep on caching internals. The main problem was implementing an LRU cache from scratch, and then they kept pulling the thread with persistence, serialization trade-offs, and atomicity. More to unpack than I expected.

Questions Asked (3)

Implement an LRU cache in Python that works as a decorator or class, handles both positional and keyword arguments correctly, and maps semantically equivalent calls to the same cache entry regardless of keyword ordering or argument type.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The canonical key part tripped me up more than the data structure itself.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then design a class-based LRU cache using an OrderedDict or doubly linked list with a dictionary. Implement a decorator that normalizes function arguments into a canonical key, ensuring semantically equivalent calls map to the same entry. Discuss trade-offs like thread safety, memory management, and performance.

Pro tip: Mention that you would use functools.lru_cache as a reference but highlight its limitations (e.g., no support for unhashable arguments, no custom key normalization) to show depth. Also, consider using a sentinel object to distinguish between missing keys and cached None values.

1. Clarify Requirements and Edge Cases

Ask about expected cache size, eviction policy, thread safety, and whether arguments can be unhashable. Discuss how to handle mutable arguments and default values.

2. Design the Cache Data Structure

Choose an OrderedDict for O(1) operations, or implement a doubly linked list with a hash map. Explain how to maintain recency and evict the least recently used item when capacity is exceeded.

3. Implement Argument Normalization

Create a canonical key from *args and **kwargs by sorting keyword arguments and converting them to a hashable form (e.g., tuple of sorted items). Handle unhashable arguments by serializing them or raising an error.

4. Build the Decorator or Class Interface

Wrap the cache logic in a decorator that can be applied to functions, or as a class that can be instantiated. Ensure it preserves the original function's metadata using functools.wraps.

5. Discuss Trade-offs and Extensions

Talk about thread safety (e.g., using locks), memory overhead, and alternative eviction policies (LFU, TTL). Mention how to test the cache for correctness and performance.

Key Points to Mention

Use of OrderedDict or doubly linked list + hash map for O(1) get and put operations.
Canonical key generation: sorting kwargs and converting to a hashable tuple, handling unhashable types via serialization or custom hashing.
Decorator implementation with functools.wraps to preserve function metadata and support both positional and keyword arguments.
Thread safety considerations: using threading.Lock or RLock to protect cache operations in concurrent environments.
Memory management: setting a maximum size, eviction policy, and potential use of weak references for large objects.
Testing strategy: unit tests for cache hits/misses, eviction order, and equivalence of calls with different argument orders.

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

Add persistence to the LRU cache so it can be snapshotted to disk and restored when the process restarts. What on-disk format would you use, how do you version it, and how do you write atomically?

System DesignTechnical Trade-offs

Author's notes

Atomic writes I knew: write to a temp file, then rename.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what data must persist (keys, values, metadata like recency order), acceptable restore time, and consistency guarantees. Then propose a concrete format (e.g., length-prefixed binary with a header), explain versioning via a magic number and version field, and describe atomic writes using write-to-temp + fsync + rename. Emphasize trade-offs between simplicity, performance, and robustness.

Pro tip: Mention that you'd fsync the directory after rename to ensure the rename itself is durable, and consider checksums to detect corruption. This shows deep systems knowledge and attention to failure modes.

1. Clarify requirements and constraints

Ask about expected cache size, read/write throughput, acceptable downtime during snapshot, and whether the cache can be reconstructed from source if persistence fails. This scopes the design.

2. Choose on-disk format

Propose a format: e.g., a header with magic bytes, version, and metadata (entry count, checksum), followed by serialized entries. Use length-prefixed binary for efficiency or JSON for simplicity, depending on constraints.

3. Design versioning strategy

Include a version number in the header. On load, check version and either migrate, reject, or fall back to empty cache. Discuss forward/backward compatibility and migration paths.

4. Implement atomic writes

Write to a temporary file in the same directory, fsync the file, then atomically rename it over the target. Optionally fsync the directory to ensure the rename is durable. Handle cleanup of temp files on failure.

5. Address restore and failure handling

On startup, attempt to load the snapshot; if missing or corrupt, start with an empty cache. Consider lazy loading or background restore if the cache is large to avoid blocking startup.

Key Points to Mention

Use a magic number and version field in the header for format identification and versioning.
Atomic write pattern: write to temp file, fsync, rename, and optionally fsync directory.
Include a checksum (e.g., CRC32) to detect corruption and handle gracefully.
Serialize both key-value pairs and LRU metadata (e.g., access order) to preserve eviction order.
Consider performance: binary format for speed/size vs. JSON for debuggability; compression if needed.
Handle partial writes and crashes: temp file cleanup, idempotent restore, and fallback to empty cache.

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

Compare pickle versus a JSON-based serialization approach for persisting the cache. Walk through the trade-offs around security, forward/backward compatibility, and performance.

Technical Trade-offsSystem Design

Author's notes

Pickle is faster and handles arbitrary Python objects natively, but loading a pickle file from an untrusted source is basically arbitrary code execution.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by framing the decision as a trade-off between convenience and safety, then systematically compare pickle and JSON across security, compatibility, and performance. Use concrete examples to illustrate risks and benefits, and conclude with a recommendation based on the cache's trust boundary and evolution needs.

Pro tip: Emphasize that pickle is unsafe for untrusted data and brittle across code changes, while JSON is safer and more interoperable but may require custom encoding for complex types. Mention that for high-performance caches, alternatives like MessagePack or Protocol Buffers can offer a middle ground.

1. Define the context and requirements

Clarify what the cache stores, who can write to it, and how it will evolve. This sets the criteria for evaluating pickle vs JSON.

2. Analyze security implications

Discuss pickle's arbitrary code execution risk if data is untrusted, versus JSON's safety as a data-only format.

3. Evaluate compatibility and schema evolution

Compare pickle's Python-version and class-definition dependencies with JSON's human-readable, language-agnostic nature and need for explicit versioning.

4. Compare performance characteristics

Contrast pickle's speed and native support for complex objects with JSON's slower serialization/deserialization and limited type support.

5. Synthesize and recommend

Weigh the trade-offs against the cache's use case and propose a solution, possibly a hybrid or alternative format.

Key Points to Mention

Pickle can execute arbitrary code during deserialization, making it unsafe for untrusted data; JSON is safe as it only serializes basic data types.
Pickle is Python-specific and tightly coupled to class definitions, causing breakage when code changes; JSON is language-agnostic and more resilient to schema evolution with proper versioning.
Pickle is generally faster and handles complex Python objects natively; JSON requires custom encoders/decoders and is slower, especially for large data.
JSON is human-readable and easier to debug, while pickle produces binary data that is not human-readable.
For caches, consider the trust boundary: if the cache is only written by trusted internal processes, pickle's risks may be acceptable; otherwise, JSON or safer alternatives like MessagePack are preferable.
Alternatives like MessagePack, Protocol Buffers, or Avro can offer better performance than JSON while maintaining safety and cross-language compatibility.

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