Meta·Software Engineer·Onsite - Coding / Algorithms·Senior

Senior

Apr 2026

Summary

Meta software engineer round 3, AI coding. The problem was a scheduling/optimization thing involving orders, deadlines, worker proficiency across steps, and maximizing points. Not the kind of problem you see every day.

Questions Asked (1)

You have a set of products, each requiring multiple steps in sequence. Each step takes a different amount of time and needs a specific workstation. Workers have varying proficiency levels for different steps. Given a list of orders with associated deadlines and point values, design an approach to maximize total score by completing as many orders as possible before their deadlines.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This is basically a scheduling optimization problem with a bunch of constraints layered on top of each other.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and assumptions, then decompose it into scheduling and assignment subproblems. Propose a greedy algorithm for order selection based on deadlines and points, and a matching or heuristic approach for worker-step assignment. Discuss trade-offs between optimality and computational complexity, and suggest how to handle dynamic changes.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that perfect optimization may be NP-hard, so a practical solution might combine greedy scheduling with local search or integer programming for smaller instances. Also, emphasize the importance of monitoring and adapting to changes in a production system.

1. Clarify Requirements and Constraints

Ask questions to understand the problem fully: Are orders independent? Can workstations handle multiple steps? Are worker proficiencies static? What are the time units? This ensures you address the right problem.

2. Model the Problem

Formalize as an optimization problem: maximize sum of points of completed orders subject to deadlines and resource constraints. Identify it as a variant of job scheduling with sequence-dependent processing times and assignment constraints.

3. Propose a Solution Strategy

Outline a two-phase approach: first, select orders using a priority rule (e.g., earliest deadline first, highest points per unit time) and schedule them; second, assign workers to steps using a matching algorithm (e.g., Hungarian) or greedy heuristic based on proficiency.

4. Analyze Complexity and Trade-offs

Discuss the computational complexity of the proposed algorithms. If exact optimization is infeasible, suggest approximation algorithms or heuristics, and explain the trade-off between solution quality and runtime.

5. Consider Extensions and Scalability

Mention how to handle dynamic order arrivals, worker availability changes, or system failures. Suggest using a streaming or online algorithm, and possibly machine learning to predict processing times.

Key Points to Mention

Greedy algorithms for scheduling (e.g., earliest deadline first, weighted shortest processing time)
Assignment problem and algorithms like Hungarian algorithm or min-cost max-flow
NP-hardness of integrated scheduling and assignment, and use of heuristics or approximation
Trade-offs between optimality, fairness, and computational efficiency
Dynamic re-optimization and online algorithms for real-time changes
Use of priority queues and data structures for efficient order selection

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