Meta·Machine Learning Engineer·Onsite - System Design / Architecture·Staff

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Jun 2026

Summary

Brutal system design round at Meta for an MLE role. One massive question that basically asked me to design an entire MoE pretraining stack from scratch, covering architecture, parallelism, fault tolerance, and scaling. Felt like they wanted a staff-level answer in 45 minutes.

Questions Asked (1)

Design a large-scale Mixture-of-Experts pretraining pipeline for a bilingual LLM trained on roughly 1 trillion tokens across 256 A100 GPUs. Cover model architecture, parallelism strategy, communication patterns, load balancing, memory and optimizer sharding, checkpointing, dataset curation, tokenization, training schedule, and monitoring. Also discuss common failure modes and how you'd scale the system to 1,024 GPUs.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This was basically the whole interview.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then structure your answer around the key components: model architecture, parallelism, data, training, and scaling. Emphasize trade-offs and justify decisions based on the 1T token budget and 256 A100 GPUs. Conclude with failure modes and scaling to 1,024 GPUs.

Pro tip: Demonstrate awareness of real-world bottlenecks like communication overhead and load imbalance, and propose concrete mitigation strategies such as expert capacity factors and gradient compression. Show that you consider both efficiency and model quality.

1. Clarify Requirements and Constraints

Ask about model size, expert count, token distribution, and hardware specifics to tailor your design. Confirm goals like training time, cost, and performance targets.

2. Design Model Architecture and Parallelism

Propose a MoE architecture with top-k gating and specify parallelism dimensions (data, tensor, pipeline, expert). Explain how to combine them for 256 GPUs.

3. Address Data and Training Pipeline

Outline dataset curation, tokenization (e.g., SentencePiece), and training schedule (learning rate, batch size). Discuss load balancing and memory optimization.

4. Implement Checkpointing and Monitoring

Describe checkpointing strategy (sharded, asynchronous) and monitoring tools (loss, throughput, expert utilization). Include failure recovery.

5. Discuss Failure Modes and Scaling

Identify common failures (stragglers, load imbalance, communication bottlenecks) and solutions. Explain scaling to 1,024 GPUs with adjustments.

Key Points to Mention

Expert parallelism and all-to-all communication patterns
Load balancing techniques: auxiliary loss, capacity factor, expert dropout
Memory optimization: ZeRO optimizer, activation checkpointing, mixed precision
Dataset curation: deduplication, quality filtering, bilingual balancing
Training schedule: warmup, cosine decay, batch size scaling
Scaling strategies: hierarchical all-to-all, topology-aware placement, fault tolerance

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