Google·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Went through a technical screen for an ML Engineer role at Google that was basically a deep dive into how LLMs actually work, soup to nuts. One long question that branched into a dozen sub-topics. Felt like a PhD oral exam more than an interview.

Questions Asked (1)

Walk through the full lifecycle of a large language model, from data collection through deployment, covering pretraining, architecture choices, fine-tuning, and preference optimization. What are the key trade-offs across quality, safety, latency, cost, and scalability?

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This is the kind of question that sounds manageable until you're three minutes in and realize you've only covered data filtering and you have like eight more topics to go.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a chronological pipeline, but weave trade-offs into each stage rather than listing them separately. Emphasize how decisions at each stage (e.g., data quality, architecture, fine-tuning method) impact downstream metrics like safety, latency, and cost. Conclude by discussing how you would prioritize trade-offs based on product requirements and constraints.

Pro tip: Demonstrate systems thinking by explicitly connecting early-stage decisions (e.g., data deduplication, tokenizer choice) to late-stage outcomes (e.g., inference cost, safety alignment). This shows you understand the full stack and can anticipate second-order effects.

1. Data Collection and Preprocessing

Describe sourcing diverse, high-quality data at scale, including deduplication, filtering, and tokenization. Discuss trade-offs: larger datasets improve quality but increase cost and may introduce bias; aggressive filtering reduces toxicity but can hurt diversity.

2. Model Architecture and Pretraining

Explain architecture choices (e.g., transformer variants, sparse attention, mixture-of-experts) and pretraining objectives (e.g., causal language modeling). Trade-offs: larger models improve quality but increase latency, cost, and carbon footprint; efficient architectures reduce cost but may sacrifice performance.

3. Fine-Tuning and Adaptation

Cover supervised fine-tuning (SFT) on task-specific data and parameter-efficient methods (e.g., LoRA, adapters). Trade-offs: full fine-tuning yields better quality but is costly and risks catastrophic forgetting; PEFT reduces cost but may underperform on complex tasks.

4. Preference Optimization and Safety Alignment

Discuss methods like RLHF, DPO, and constitutional AI to align model outputs with human preferences and safety guidelines. Trade-offs: stronger alignment improves safety but can reduce diversity and increase training complexity; over-optimization may lead to reward hacking.

5. Deployment and Serving

Explain deployment strategies: model compression (quantization, pruning), distillation, caching, and autoscaling. Trade-offs: compression reduces latency and cost but may degrade quality; larger models improve quality but require more resources and may violate latency SLAs.

Key Points to Mention

Data quality and diversity directly impact model fairness and safety; techniques like deduplication and toxicity filtering are essential but require balancing with data volume.
Architecture choices (e.g., MoE, sparse attention) can decouple model capacity from inference cost, but introduce training complexity and potential load-balancing issues.
Fine-tuning methods: full fine-tuning vs. parameter-efficient fine-tuning (PEFT) — trade-offs in quality, cost, and catastrophic forgetting.
Preference optimization (RLHF, DPO) improves alignment but can reduce output diversity and increase training instability; reward modeling is critical.
Deployment optimizations (quantization, distillation, caching) reduce latency and cost but may sacrifice accuracy; need to measure impact on business metrics.
Scalability considerations: distributed training, model parallelism, and serving infrastructure must handle increasing model sizes and traffic without proportional cost increases.

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