Anthropic OA Latest Review: Inference Engine + Extra Trees Debug

Just finished the latest Anthropic OA and wanted to share a detailed breakdown while the experience is still fresh.

At first glance, these two problems may look unfamiliar because they are not typical LeetCode-style questions. But both are very aligned with Anthropic’s interview style: instead of testing memorized algorithms, they focus on whether you can understand AI systems, debug machine learning code, and handle engineering details correctly.

Problem 1: Inference Engine (Request Scheduler)

The first problem is essentially building a simplified GPU request scheduler.

When a user request arrives, it first goes through the Prefill stage, where the model processes the input and builds the KV Cache. After that, it enters the Decode stage, generating one token at a time.

The GPU has a limited token processing capacity per timestep (batch capacity), and your job is to decide which requests should be scheduled onto the GPU at each step.

This is not asking you to implement a full LLM serving system like vLLM. The key is getting the scheduling logic correct and making sure the request state transitions are handled properly.

The Four Most Common Failure Points

1. Mixing Up Request States

Each request needs a clear lifecycle:

Waiting → Prefilling → Decoding → Finished

A request cannot enter Decode before Prefill is completed. Incorrect state transitions are one of the easiest ways to fail hidden tests.

2. Not Removing Finished Requests

Completed requests must be removed from active queues immediately. Keeping finished requests around can affect later scheduling decisions and cause unexpected failures.

3. Exceeding Batch Capacity

You cannot put unlimited requests into one timestep. If the current batch capacity is full, remaining requests must wait for the next scheduling round.

4. Overengineering the Solution

A common mistake is trying to simulate a real production LLM engine. The OA only evaluates scheduler correctness. A clean state machine with proper queue management is enough.

Implementation Approach

A simple and reliable approach:

• Maintain explicit request states.
• Use queues to manage waiting and active requests.
• At each timestep, process available prefill/decode work.
• Update states after execution.
• Remove finished requests immediately.

The core idea is treating the scheduler as a state machine rather than trying to model every detail of an inference engine.

Problem 2: Debug Extremely Randomized Trees

The second problem gives you an incomplete or buggy implementation of Extremely Randomized Trees (Extra Trees) and asks you to fix the code until all tests pass.

This is not a from-scratch implementation problem. The challenge is reading unfamiliar ML code, understanding the intended behavior, and locating subtle bugs.

Three Common Bug Categories

1. Missing Edge Case Handling

Many failures come from unhandled edge cases:

• Empty datasets
• Only one sample remaining
• Nodes that cannot be split further

Before splitting a node, always verify whether the current data can actually be partitioned.

2. NumPy Shape Issues

NumPy dimension bugs are extremely common in ML code.

For example:

(10,)
(10, 1)

These may look similar but behave differently in indexing, broadcasting, and matrix operations.

Be careful with operations like squeeze() and reshape(), and keep the data dimensions consistent throughout the implementation.

3. Random Split Creating Empty Children

Extra Trees randomly selects split features and thresholds. Some random splits can produce an empty left or right child.

If this case is not handled, recursive tree construction can fail.

Recommended Debugging Process

1. Run all provided tests and identify failing cases.
2. Start fixing from the smallest edge cases.
3. After every change, rerun tests.
4. Keep modifications minimal instead of rewriting the entire implementation.

Large-scale rewrites usually introduce more bugs. The goal is to understand the existing code and repair it efficiently.

What This OA Really Tests

These two problems represent Anthropic’s engineering-focused interview style.

The first problem checks whether you understand fundamental LLM inference concepts:

• Prefill
• Decode
• KV Cache
• Batching
• Request scheduling

The second problem checks whether you can work with real machine learning code:

• Debug existing implementations
• Handle edge cases
• Understand recursive algorithms
• Work carefully with NumPy

This is not just about solving algorithm puzzles. It is about reading systems, understanding requirements, and writing reliable engineering code.

Preparation Advice

For students preparing for Anthropic, OpenAI, and other AI-focused companies, these areas are worth prioritizing:

• Understand basic LLM inference concepts: Prefill, Decode, KV Cache, Continuous Batching.
• Practice reading and debugging existing ML code.
• Be comfortable with tree models and recursive implementations.
• Pay close attention to NumPy shapes and boundary conditions.
• Build the habit of writing explicit state transitions and defensive checks.

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