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

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May 2026

Summary

Brutal system design loop at Meta for an MLE role. The whole thing was basically one massive open-ended question about image generation that kept branching into multimodal territory, and they expected you to go deep on both the research side and the production side.

Questions Asked (3)

Design an end-to-end image generation system, covering data collection and curation, model architecture, training objectives, compute planning, safety filtering, evaluation, and inference optimization.

System DesignTechnical Trade-offs

Author's notes

This question is enormous and I think I underestimated that at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., resolution, latency, scale, safety policies) to frame the design. Then walk through the pipeline end-to-end, justifying key architectural and training choices with trade-offs. Emphasize scalability, safety, and evaluation as first-class concerns.

Pro tip: Anchor your design in Meta's scale and product context—mention how you'd leverage existing infrastructure (e.g., PyTorch, FAISS for retrieval, large-scale training) and how you'd handle the unique challenges of a social platform (e.g., safety, diverse user base).

1. Clarify Requirements and Constraints

Ask about target resolution, generation speed, deployment environment, safety policies, and scale (e.g., billions of images). This ensures your design meets actual needs.

2. Data Collection and Curation

Describe sourcing diverse, high-quality data (e.g., licensed images, user data with consent), filtering (NSFW, duplicates, bias), and annotation (alt-text, captions). Mention deduplication and balancing.

3. Model Architecture and Training

Choose an architecture (e.g., diffusion models, GANs, autoregressive) and justify. Outline training objectives (e.g., denoising score matching, adversarial loss), compute planning (GPU/TPU clusters, distributed training), and hyperparameters.

4. Safety Filtering and Evaluation

Integrate safety filters (input/output moderation, watermarking) and evaluation metrics (FID, CLIP score, human eval, bias audits). Discuss iterative improvement.

5. Inference Optimization and Deployment

Optimize for latency and cost: model quantization, distillation, caching, batching, and hardware acceleration. Discuss serving infrastructure and monitoring.

Key Points to Mention

Trade-offs between model size, quality, and inference speed (e.g., diffusion steps vs. latency).
Data quality and diversity: importance of curation, deduplication, and bias mitigation.
Safety: multi-layered filtering (pre-training data, post-generation), watermarking, and policy compliance.
Evaluation: both automated metrics (FID, IS, CLIP) and human evaluation for subjective quality and safety.
Compute planning: distributed training strategies (data/model parallelism), mixed precision, and cost estimation.
Inference optimization: techniques like quantization, knowledge distillation, and efficient sampling (e.g., DDIM).

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

Extend your image generation design to support multimodal input and output, where the system can both accept and produce text and images together.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

Felt like a follow-up but was really a second full question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and scope of multimodal support, then propose a unified architecture that handles both text and images as inputs and outputs. Discuss trade-offs in model design, training, and serving, and outline how to evaluate and iterate on the system.

Pro tip: Emphasize the importance of a shared latent space for cross-modal understanding and generation, and discuss how to handle modality-specific preprocessing and postprocessing efficiently at scale.

1. Clarify Requirements

Ask questions to understand expected use cases, modalities, latency, scale, and quality metrics. Define what 'multimodal' means for this system (e.g., text-to-image, image-to-text, joint generation).

2. High-Level Architecture

Propose a unified model or ensemble that can process and generate both text and images. Consider using a shared encoder-decoder with modality-specific heads, or a diffusion model conditioned on text and image embeddings.

3. Model Design & Training

Discuss how to train such a model: joint training on paired and unpaired data, contrastive learning for alignment, and techniques like CLIP for shared representations. Address challenges like modality imbalance and catastrophic forgetting.

4. Serving & Infrastructure

Outline how to serve the model efficiently: handling variable input/output sizes, batching, caching, and scaling. Consider using separate services for text and image processing if needed, but with a unified API.

5. Evaluation & Iteration

Define metrics for both modalities (e.g., FID, CLIP score, BLEU) and human evaluation. Discuss A/B testing and feedback loops to improve the system over time.

Key Points to Mention

Shared latent space and cross-modal alignment techniques (e.g., CLIP, contrastive loss)
Trade-offs between a single unified model vs. separate models for each modality
Handling variable-length and high-dimensional inputs/outputs in training and serving
Data requirements: paired and unpaired multimodal datasets, data augmentation
Evaluation metrics for multimodal generation (e.g., FID, IS, CLIP score, human evaluation)
Scalability and latency considerations for real-time multimodal applications

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

Walk through a specific recent paper relevant to your design: explain the key idea, experimental setup, metrics used, trade-offs, and how you would adapt it for production.

Technical Trade-offsSystem Design

Author's notes

The part I was least prepared for.

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AI HintsAI Generated

Suggested Approach

Select a recent paper that directly relates to the design problem at hand, and structure your answer to first explain the core idea and experimental setup, then critically analyze the metrics and trade-offs, and finally propose a concrete adaptation plan for production. Emphasize how you would address scalability, latency, and reliability constraints in a production environment.

Pro tip: Choose a paper you can critique, not just summarize—showing you understand its limitations and can improve upon it demonstrates senior-level thinking. Also, explicitly connect the paper's trade-offs to Meta's scale and product constraints to show you're already thinking like an engineer there.

1. Summarize the paper's key idea

Briefly state the problem the paper addresses and its main contribution, focusing on why it's relevant to the design question. Avoid deep technical jargon; aim for clarity.

2. Describe the experimental setup and metrics

Explain the datasets, baselines, and evaluation metrics used, and comment on whether they are appropriate for the problem. Highlight any potential biases or limitations in the evaluation.

3. Analyze trade-offs

Discuss the trade-offs the paper makes, such as accuracy vs. efficiency, complexity vs. interpretability, or training cost vs. inference speed. Relate these to real-world constraints.

4. Propose production adaptation

Outline how you would adapt the method for production, including changes to handle scale, latency, data distribution shifts, and monitoring. Mention specific engineering considerations like model serving, retraining, and A/B testing.

5. Conclude with impact and next steps

Summarize the potential impact of your adaptation and suggest metrics to track success. Optionally, mention alternative approaches you considered.

Key Points to Mention

The paper's core innovation and its relevance to the design problem
Experimental setup: datasets, baselines, and evaluation metrics
Trade-offs: e.g., accuracy vs. latency, model size vs. performance, training cost vs. inference efficiency
Production challenges: scalability, reliability, data drift, and monitoring
Adaptation strategy: specific modifications for production, such as quantization, distillation, or distributed training
Success metrics and potential A/B testing plan

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