RetellAI·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

RetellAI software engineering interview with a system design question that had a fairness angle I wasn't really prepared for. Decent question, made me think harder than I expected.

Questions Asked (1)

Design a scheduling system that assigns tasks to workers or workers to shifts. Beyond just making it work, the schedule needs to be 'fair': balanced workload, no one repeatedly stuck with the worst slots, no consecutive overnight shifts, etc. Define what fairness means, propose an algorithm, and walk through the trade-offs compared to a pure greedy approach or a full optimization solver.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The fairness definition part tripped me up first.

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AI HintsAI Generated

Suggested Approach

Start by defining fairness concretely as a set of measurable constraints and objectives (e.g., max workload variance, no consecutive overnights, equitable distribution of undesirable shifts). Then propose a two-phase algorithm: first, a greedy assignment that respects hard constraints, followed by a local search or optimization step to improve fairness metrics. Finally, discuss trade-offs between greedy (fast but unfair), full optimization (fair but slow and brittle), and your hybrid approach (balanced).

Pro tip: Acknowledge that fairness is multi-dimensional and often requires stakeholder input to prioritize; propose a tunable fairness score that can be adjusted based on feedback, showing you understand real-world product constraints.

1. Define Fairness Metrics

Translate vague fairness into quantifiable metrics: e.g., workload variance, max consecutive undesirable shifts, distribution of weekend/night shifts, and preference satisfaction. Prioritize these with stakeholders.

2. Model Constraints and Objectives

List hard constraints (e.g., no consecutive overnights, legal rest periods) and soft constraints (e.g., worker preferences). Formulate as a constraint satisfaction and optimization problem.

3. Propose a Hybrid Algorithm

Use a greedy heuristic to quickly generate a feasible schedule, then apply local search (e.g., simulated annealing) or integer programming to optimize fairness metrics while maintaining hard constraints.

4. Analyze Trade-offs

Compare greedy (fast, simple, but poor fairness), full optimization (optimal fairness, but slow and may not scale), and hybrid (balanced, scalable, tunable). Discuss complexity, scalability, and adaptability.

5. Address Practical Considerations

Mention handling dynamic changes (sick leave, shift swaps), explainability of assignments, and potential for human-in-the-loop adjustments.

Key Points to Mention

Fairness is context-dependent and should be defined with measurable metrics (e.g., variance, max consecutive nights).
Hard constraints (legal, safety) must always be satisfied; soft constraints (preferences) are optimized.
Greedy algorithms are fast but can lead to unfair distributions; full optimization (e.g., ILP) guarantees optimality but may be computationally expensive.
Hybrid approaches (greedy + local search) offer a good balance of speed and fairness.
Scalability: consider problem size (workers x shifts) and use approximation algorithms for large instances.
Explainability and tunability: allow adjustments to fairness weights and provide reasons for assignments.

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