Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

OpenAI software engineering interview that was essentially a system design question dressed up as a coding problem. You're building a mini in-memory database from scratch and then they start pulling on threads about indexes and query extensibility.

Questions Asked (1)

Design and implement a simple in-memory database that stores records with multiple fields and supports filtering by any column value. Walk through your API design choices and how you'd extend it to handle compound AND/OR filters and indexed lookups.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

Started with a straightforward insert/query interface, dict keyed by id, and a linear scan over all records to match filters.

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Suggested Approach

Start by clarifying requirements and defining a minimal but extensible API, then walk through a simple implementation using a list of records and linear scans. Explain how to extend it with a filter expression tree for compound AND/OR and add indexes (e.g., hash maps) for fast lookups on specific columns. Emphasize trade-offs between simplicity, performance, and flexibility.

Pro tip: Show that you think about extensibility and performance from the start: mention that you'd design the filter interface to be composable (e.g., using the Composite pattern) so that adding AND/OR later doesn't require rewriting the core. Also, discuss how indexes can be added incrementally without breaking the API.

1. Clarify requirements and constraints

Ask about expected data volume, query patterns, concurrency needs, and whether persistence is required. This sets the stage for design decisions.

2. Design the core API

Define methods for inserting records, retrieving all records, and filtering by column value. Consider using a generic record type (e.g., map or struct) and a filter predicate interface.

3. Implement basic filtering

Describe a simple implementation: store records in a list, and for filtering, iterate and apply the predicate. Discuss time complexity O(n) per query.

4. Extend to compound filters

Introduce a filter expression tree with AND/OR nodes and leaf nodes for column comparisons. Show how to evaluate recursively, enabling arbitrary combinations.

5. Add indexed lookups

Explain how to maintain secondary indexes (e.g., hash maps from column value to record IDs) for columns that are frequently queried. Discuss trade-offs: faster reads, slower writes, and memory overhead.

Key Points to Mention

API design: methods like insert(record), query(filter), and possibly createIndex(column).
Filter abstraction: use a Predicate or Expression interface to allow composable filters.
Compound filters: implement AND/OR using the Composite pattern, with short-circuit evaluation.
Indexing: hash indexes for equality lookups; B-trees for range queries if needed.
Trade-offs: linear scan vs. index lookup, memory vs. speed, write amplification.
Extensibility: how to add new filter types or index types without breaking existing code.

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