TikTok·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

TikTok Data Scientist interview with a scheduling algorithm problem dressed up as a game scenario. The core was a real algorithmic challenge about job dispatching across parallel machines, so don't let the 'Production Factory' framing fool you into thinking it's a product case.

Questions Asked (1)

You have jobs arriving with different processing times that need to be assigned to one of two parallel machines in real time. How do you design an algorithm that minimizes average flow time across all jobs?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew greedy was the right instinct but fumbled explaining why shortest-processing-time works for flow time specifically versus makespan.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the objective and constraints, then propose a greedy algorithm that assigns each arriving job to the machine with the smallest current load, leveraging the Shortest Processing Time (SPT) rule to minimize average flow time. Discuss the algorithm's optimality under certain conditions and its practical implementation, including handling real-time arrivals and potential trade-offs.

Pro tip: Mention that while the greedy approach is optimal for minimizing average flow time on identical parallel machines when preemption is allowed, for non-preemptive scheduling it's a 2-approximation; showing awareness of these nuances demonstrates depth.

1. Clarify the problem and assumptions

Restate the problem: jobs arrive over time with known processing times, assigned in real-time to two identical parallel machines, goal is to minimize average flow time (sum of completion times minus arrival times). Confirm if preemption is allowed, if processing times are known upon arrival, and if machines are identical.

2. Identify the objective and relevant scheduling theory

Average flow time is minimized by the Shortest Processing Time (SPT) rule on a single machine. For parallel machines, the problem is NP-hard in general, but greedy heuristics like List Scheduling (assign to least loaded machine) perform well. For identical machines and preemption, SPT with preemption (SRPT) is optimal.

3. Propose a greedy algorithm

Design an algorithm that, upon each job arrival, assigns it to the machine with the smallest current total processing time (load). This is the List Scheduling algorithm. If preemption is allowed, consider preemptive SPT: always process the job with the shortest remaining processing time on any available machine.

4. Analyze performance and trade-offs

Discuss that List Scheduling is a 2-approximation for minimizing average flow time on identical machines. For preemptive case, SRPT is optimal. Mention that in practice, the greedy approach is simple, online, and efficient (O(log n) with priority queues).

5. Consider extensions and practical implementation

Address real-world factors: unknown future arrivals, varying job sizes, potential machine heterogeneity. Suggest using a priority queue to track machine loads, and possibly machine learning to predict processing times if not known. Conclude with recommendation based on assumptions.

Key Points to Mention

Shortest Processing Time (SPT) rule minimizes average flow time on a single machine.
List Scheduling (assign to least loaded machine) is a common greedy heuristic for parallel machines.
For preemptive scheduling on identical machines, Shortest Remaining Processing Time (SRPT) is optimal.
The non-preemptive parallel machine scheduling problem is NP-hard, so heuristics are used.
List Scheduling has a 2-approximation guarantee for minimizing average flow time.
Implementation can use a min-heap to track machine loads for O(log n) assignment per job.

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