Scale.ai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Scale.ai ML engineer interview that went deep on post-training fundamentals. One meaty technical question on supervised fine-tuning that covered a lot of ground, felt more like a whiteboard lecture than a back-and-forth.

Questions Asked (1)

Walk me through supervised fine-tuning for large language models, including the training objective, how you prepare and format data, what hyperparameters matter, common failure modes, and where it fits in the broader post-training pipeline.

Technical Trade-offsSystem DesignAlgorithms & Data Structures

Author's notes

This question sprawled in a way I wasn't ready for.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the SFT pipeline: start with the training objective and data preparation, then cover hyperparameters and failure modes, and finally position SFT within the broader post-training stack. Emphasize practical trade-offs and how you would debug issues, since Scale.ai values hands-on engineering judgment.

Pro tip: Mention that SFT is often the first step of post-training and that its quality directly impacts later stages like RLHF or DPO—showing you understand the full pipeline and not just isolated fine-tuning.

1. Define the objective and loss

Explain that SFT minimizes the negative log-likelihood of the target response tokens given the prompt, typically using cross-entropy loss with teacher forcing. Clarify that only the response tokens contribute to the loss, not the prompt tokens.

2. Data preparation and formatting

Describe how to curate high-quality demonstration data, format it into prompt-response pairs with a consistent chat template, and handle special tokens. Mention data cleaning, deduplication, and balancing across tasks.

3. Hyperparameters and training setup

Cover key hyperparameters: learning rate (often small, e.g., 1e-5 to 5e-5), batch size, number of epochs (typically 1-3), warmup, weight decay, and whether to use LoRA or full fine-tuning. Discuss packing sequences for efficiency and using gradient checkpointing.

4. Common failure modes and mitigations

Identify issues like overfitting, catastrophic forgetting, exposure bias, and reward hacking (if later RLHF). Explain how to detect them (e.g., validation loss, generation quality) and mitigate (e.g., early stopping, data diversity, regularization).

5. Position in post-training pipeline

Explain that SFT is typically the first post-training step, followed by preference optimization (RLHF/DPO) and possibly safety tuning. Highlight that SFT teaches format and basic behavior, while later stages refine alignment.

Key Points to Mention

Cross-entropy loss on response tokens only, with prompt tokens masked
Data quality over quantity: diverse, high-quality demonstrations with consistent formatting
Hyperparameters: low learning rate, few epochs, batch size, and use of LoRA for efficiency
Failure modes: overfitting, catastrophic forgetting, and exposure bias
SFT as the foundation for RLHF/DPO, enabling format learning and basic instruction following
Practical tips: sequence packing, gradient checkpointing, and monitoring validation loss

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