Anthropic·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

Interviewed for an ML Engineer role at Anthropic and got a coding problem that looked deceptively simple at first glance. The follow-up is what really got me thinking.

Questions Asked (2)

Given a sequence of function calls, convert them into a timeline of start and end events, where each call generates a corresponding start event and end event. Output the full event sequence in order.

Algorithms & Data Structures

Author's notes

Seemed straightforward until I started thinking about ordering edge cases.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and whether calls are nested or sequential, then model each call as a pair of start and end events. Use a stack to handle nesting and produce the event sequence in the correct order, ensuring that start events precede their corresponding end events.

Pro tip: Explicitly state your assumptions about the input (e.g., calls are properly nested, no overlapping calls) and mention that the solution can be adapted if the input is a flat list with parent references. This shows you think about edge cases and real-world data.

1. Clarify input format and constraints

Ask whether the sequence is given as a nested structure or a flat list with parent pointers, and whether calls can be sequential or only nested. Confirm if the output should be a list of events with timestamps or just the order.

2. Define event representation

Decide on a data structure for events, such as a tuple (call_id, event_type, timestamp) or a simple string like 'start call1'. Ensure each call generates exactly one start and one end event.

3. Choose traversal strategy

If the input is nested, use recursion or an explicit stack to traverse the call tree. If it's a flat list with parent references, build a tree first or use a stack to simulate nesting.

4. Generate events in order

During traversal, emit a start event when entering a call and an end event when leaving. For sequential calls, process them in order, ensuring each call's start and end are adjacent if not nested.

5. Handle edge cases and validate

Consider empty input, single call, deeply nested calls, and malformed input (e.g., end without start). Validate that the output has balanced start/end events and matches the call structure.

Key Points to Mention

Use a stack to manage nested calls and ensure proper ordering of start and end events.
Clarify whether calls are sequential or nested, as this affects the event sequence.
Define a clear event representation, such as (call_id, event_type, timestamp).
Discuss time complexity: O(n) where n is the number of calls, as each call generates two events.
Mention edge cases like empty input, single call, and deeply nested calls.
If input is a flat list with parent references, explain how to reconstruct the call tree or simulate nesting with a stack.

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

Follow-up: modify your solution so that only runs of the same function call that meet or exceed a given threshold count are emitted as events. Runs shorter than the threshold should be merged or ignored.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where I stumbled.

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AI HintsAI Generated

Suggested Approach

First, clarify the semantics of 'merged or ignored' and the threshold parameter, then adapt your existing solution by adding a buffering mechanism that accumulates runs until the threshold is met. Finally, discuss trade-offs between memory usage and latency, and how to handle edge cases like runs at the end of the stream.

Pro tip: Mention that you would make the threshold configurable and consider whether merging should combine adjacent short runs into a synthetic event or simply drop them, as this affects downstream ML feature engineering.

1. Clarify requirements and edge cases

Ask whether short runs should be merged with adjacent runs of the same function or ignored entirely, and how to handle runs that span the end of the input. Confirm the threshold is inclusive (>=).

2. Design a buffering strategy

Introduce a buffer to hold the current run's function name and count. Only emit an event when the run ends and its count meets the threshold; otherwise, decide to discard or merge based on clarification.

3. Adapt the existing algorithm

Modify your previous solution to track run length and compare against the threshold before emitting. Ensure the buffer is flushed appropriately at the end of the stream.

4. Analyze trade-offs

Discuss memory overhead of buffering versus latency of emitting events, and how merging short runs could create artificial events that misrepresent the data.

5. Test with examples

Walk through a few test cases: runs exactly at threshold, below threshold, alternating short runs, and a short run at the end. Verify the output matches expectations.

Key Points to Mention

Threshold semantics: inclusive vs exclusive, and whether it's configurable.
Merging strategy: combine adjacent short runs of the same function or drop them entirely.
Memory and latency trade-offs of buffering runs until threshold is met.
Handling end-of-stream: flush buffer and decide whether to emit or discard the final run.
Impact on downstream ML tasks: merging may create synthetic events that affect feature distributions.
Complexity: time O(n) and space O(1) if only buffering current run, or O(k) for merging multiple runs.

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