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Anthropic·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Anthropic software engineer interview focused on a task management system design problem. The core challenge was implementing filtering and listing with real discussion about data structure tradeoffs, not just code correctness.

Questions Asked (2)

Q1

Design and implement a task management system that supports listing tasks filtered by status, assignee, and priority in any combination, with sorting by fields like created_time or priority.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The initial implementation felt straightforward, HashMap keyed by id for O(1) lookup, then just iterate and apply predicates for filtering.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a clean API and data model that supports flexible filtering and sorting. Discuss indexing strategies and trade-offs between in-memory and database-backed approaches, and outline how to handle pagination and performance.

Pro tip: Emphasize that you would design the filtering and sorting to be composable and index-aware, and mention that you'd validate the design with concrete query patterns and performance benchmarks.

1. Clarify Requirements

Ask about expected scale (number of tasks, users), latency requirements, and whether filters/sorts are combined arbitrarily. Confirm if pagination is needed.

2. Define API and Data Model

Propose a RESTful or gRPC API with query parameters for filters and sort. Define a Task entity with fields: id, status, assignee, priority, created_time, etc.

3. Design Storage and Indexing

Choose a database (SQL or NoSQL) and design indexes to support common filter combinations and sort orders. Discuss composite indexes and trade-offs.

4. Implement Query Logic

Explain how to build dynamic queries that apply filters and sorting efficiently. Mention using query builders or ORM, and avoiding full table scans.

5. Address Scalability and Trade-offs

Discuss caching, pagination, and potential bottlenecks. Compare in-memory vs. database approaches and explain when each is appropriate.

Key Points to Mention

  • Composite indexes for common filter combinations (e.g., status + assignee + priority)
  • Sorting stability and tie-breaking (e.g., by created_time as secondary sort)
  • Pagination strategies (offset vs. cursor-based) for large result sets
  • Trade-offs between flexible filtering and performance (e.g., index explosion)
  • Caching frequently accessed queries or using materialized views
  • API design considerations: query parameter validation and default sort order

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

Q2

Walk through your data structure choices for this system. When would a linear scan with predicates be preferable over maintaining secondary indexes per field?

Technical Trade-offsData ModelingAlgorithms & Data Structures
Author's notes

They wanted a real tradeoff discussion, not just 'indexes are faster.' I talked through read-heavy vs write-heavy workloads and how indexes hurt on inserts.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the system's access patterns and constraints, then justify your data structure choices based on those requirements. Compare linear scans with secondary indexes by analyzing trade-offs in read/write performance, memory overhead, and maintenance complexity, and specify conditions where each is preferable.

Pro tip: Emphasize that indexes are not free—they add write amplification and memory cost—so the decision should be driven by the read/write ratio and query selectivity. Mention that for small datasets or infrequent queries, a linear scan can be simpler and more efficient overall.

1. Clarify requirements and access patterns

Ask about expected data volume, read/write ratio, query frequency, and latency requirements to ground your choices in real constraints.

2. Describe primary data structure

Explain the core structure (e.g., hash map, B-tree, log) and why it fits the primary access pattern, noting any secondary indexes you maintain.

3. Analyze trade-offs of secondary indexes

Discuss the overhead of maintaining indexes per field: increased write latency, memory usage, and complexity, versus faster reads for selective queries.

4. Identify when linear scan is preferable

List scenarios: small datasets, low query frequency, non-selective predicates, write-heavy workloads, or when index maintenance cost outweighs read benefits.

5. Conclude with a balanced decision rule

Summarize a heuristic: use indexes when queries are frequent and selective and writes are less critical; otherwise, favor linear scans for simplicity and write efficiency.

Key Points to Mention

  • Read/write ratio and its impact on index maintenance overhead
  • Selectivity of predicates: high selectivity favors indexes, low selectivity favors scans
  • Memory and storage overhead of secondary indexes
  • Write amplification and latency introduced by index updates
  • Query frequency and latency requirements
  • Simplicity and maintainability of linear scans for small or static datasets

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