Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

OpenAI Research Engineer interview with a meaty coding problem that took up the whole session. Four progressive sub-questions on package dependency resolution, lots of code to write, and the expectation that it'd all be modular and testable. Not a quick whiteboard exercise.

Questions Asked (1)

Build a Python package dependency resolver that takes a set of packages with version constraints (like 'numpy>=1.20,<2.0') and resolves a consistent set of versions satisfying all constraints. The problem is broken into four progressive parts: basic resolution, conflict detection, version-range intersection, and reporting unresolvable cycles.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This was basically a full coding project compressed into an interview.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then walk through a modular design that addresses each part: basic resolution, conflict detection, version-range intersection, and cycle reporting. Use a backtracking search with constraint propagation, and discuss trade-offs between completeness and performance.

Pro tip: Mention that real-world resolvers like pip use SAT solvers or PubGrub for efficiency, but for an interview, a clean backtracking solution with clear conflict reporting is often sufficient. Also, emphasize the importance of deterministic output and error messages that help users understand conflicts.

1. Clarify requirements and assumptions

Ask about input format, versioning scheme (semver?), and whether transitive dependencies are included. Confirm that the resolver should return a consistent set of versions or report conflicts.

2. Design data structures

Represent packages, versions, and constraints. Use a graph for dependencies and a constraint store that maps each package to a set of allowed versions.

3. Implement core resolution algorithm

Use backtracking search: pick an unassigned package, try versions in order, propagate constraints, and backtrack on conflict. For version-range intersection, compute the intersection of allowed ranges for each package.

4. Handle conflicts and cycles

Detect conflicts when a package has no valid versions left. For cycles, detect strongly connected components or use DFS with a recursion stack, and report the cycle path.

5. Optimize and discuss trade-offs

Discuss performance improvements like memoization, constraint propagation, or using a SAT solver. Compare completeness vs. speed and explain when to use heuristics.

Key Points to Mention

Backtracking search with constraint propagation as the core algorithm
Version-range intersection using interval arithmetic or set operations
Conflict detection and meaningful error messages (e.g., which packages conflict)
Cycle detection using DFS or Tarjan's algorithm and reporting the cycle
Trade-offs between completeness (finding a solution if one exists) and performance
Real-world resolvers (pip, PubGrub, SAT solvers) and their approaches

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