Microsoft·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Microsoft ML Engineer interview that was basically a deep dive into post-training pipelines. One long design question covering SFT and RLHF end to end. Felt more like a research discussion than a standard system design round.

Questions Asked (1)

Design a complete post-training pipeline to improve an LLM's consistency and alignment, covering supervised fine-tuning and reinforcement learning from human feedback including data sourcing, reward modeling, training choices, and evaluation.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This was a single question that ate the whole session.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: improve consistency and alignment of an LLM through a post-training pipeline. Then walk through the pipeline stages: data sourcing and curation, supervised fine-tuning (SFT), reward modeling, reinforcement learning from human feedback (RLHF), and evaluation. Emphasize trade-offs, iteration, and how you would measure success.

Pro tip: Highlight the importance of data quality and diversity over quantity, and mention that you would start with a small-scale pilot to validate the pipeline before scaling up. Also, discuss how you would handle potential reward hacking and ensure the model remains helpful, honest, and harmless.

1. Data Sourcing and Curation

Identify and collect high-quality demonstration data for SFT and comparison data for reward modeling. Ensure diversity, representativeness, and ethical considerations.

2. Supervised Fine-Tuning (SFT)

Fine-tune the base LLM on the curated demonstration data to improve instruction following and consistency. Choose appropriate hyperparameters and training objectives.

3. Reward Modeling

Train a reward model on human preference comparisons to capture desired behaviors. Address potential biases and ensure the reward model generalizes well.

4. Reinforcement Learning from Human Feedback (RLHF)

Optimize the SFT model using RL algorithms (e.g., PPO) against the reward model. Monitor for reward hacking and maintain alignment with human values.

5. Evaluation and Iteration

Evaluate the final model on consistency, alignment, and safety metrics using both automated and human evaluations. Iterate on the pipeline based on findings.

Key Points to Mention

Data sourcing: use a mix of human-written and model-generated data, ensure diversity, and apply filtering for quality and safety.
SFT training choices: consider parameter-efficient fine-tuning (e.g., LoRA) for efficiency, and use a validation set to prevent overfitting.
Reward modeling: collect pairwise comparisons, train a model to predict human preferences, and use techniques like bootstrapping to improve robustness.
RLHF training: use PPO with a KL penalty to prevent divergence from the SFT model, and monitor for reward hacking.
Evaluation: combine automated metrics (e.g., perplexity, win rates) with human evaluations for consistency, helpfulness, and harmlessness.
A/B testing: deploy the model in a controlled experiment to measure real-world impact and gather further feedback.

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