GenAI in Interviews (FAANG)

Senior GenAI engineering roles at OpenAI, Anthropic, Google DeepMind, and FAANG AI teams test a combination of: LLM architecture understanding (attention, KV cache, transformer internals), systems thinking (how to serve 1 million users with 70B models), evaluation design (how to measure alignment quality), and product judgment (when to use RAG vs fine-tuning). This lesson consolidates the mental models that distinguish senior engineers in GenAI interviews.

• OpenAI interviews include a system design question: "Design the ChatGPT backend." Expected topics: streaming token delivery, session management, abuse detection, model routing (GPT-3.5 vs GPT-4), cost per conversation.
• Anthropic's ML engineer interviews include training and alignment questions: "How does DPO differ from PPO-based RLHF and why might you choose one over the other?" - testing both algorithmic understanding and engineering trade-offs.
• Google DeepMind interviews test evaluation design: "How would you evaluate whether a new LLM is better than the current one for a medical question-answering task?" - testing benchmark design, human evaluation protocols, and safety metric design.

Предварительные знания

• GenAI system design patterns: chatbots, RAG search, copilots, scaling
• Evaluation literacy: what MMLU, HumanEval, and Arena do and do not measure
• AI safety basics: jailbreaks, guardrails, and why deployment needs monitoring

• GenAI System Design
• Evaluation and Benchmarks
• AI Safety and Alignment

How the GenAI interview became its own discipline after ChatGPT

Hiring for AI used to mean classical machine learning: feature engineering, model selection, offline metrics. ChatGPT in late 2022 changed what companies needed to build, and within a year it changed what they screened for. Through 2023 and 2024, OpenAI, Anthropic, Google DeepMind, and the AI teams at large product companies expanded headcount for people who could ship LLM features, not just train models. A new interview shape followed. Candidates were asked to design a ChatGPT-style backend, reason about RAG versus fine-tuning, estimate the cost of serving a 70B model, diagnose a latency regression, and argue safety trade-offs out loud. The signal shifted from algorithmic puzzles toward production judgment: knowing real numbers, naming trade-offs explicitly, and understanding failure modes of systems built on a stochastic model. The discipline is young, the questions are still standardizing, and the bar rewards engineers who have actually run these systems rather than only read about them.

Architecture Questions

**Architecture Questions** is a key technique in GenAI in Interviews (FAANG). It addresses specific challenges in building reliable, efficient, and scalable generative AI systems in production.

Architecture Questions is regularly tested in GenAI engineering interviews at OpenAI, Anthropic, Google DeepMind, and AI-forward product companies. Understanding the trade-offs and failure modes demonstrates production-level expertise.

Optimization Questions

**Optimization Questions** is a key technique in GenAI in Interviews (FAANG). It addresses specific challenges in building reliable, efficient, and scalable generative AI systems in production.

Optimization Questions is regularly tested in GenAI engineering interviews at OpenAI, Anthropic, Google DeepMind, and AI-forward product companies. Understanding the trade-offs and failure modes demonstrates production-level expertise.

Evaluation Questions

**Evaluation Questions** is a key technique in GenAI in Interviews (FAANG). It addresses specific challenges in building reliable, efficient, and scalable generative AI systems in production.

Evaluation Questions is regularly tested in GenAI engineering interviews at OpenAI, Anthropic, Google DeepMind, and AI-forward product companies. Understanding the trade-offs and failure modes demonstrates production-level expertise.

Product Design Questions

**Product Design Questions** is a key technique in GenAI in Interviews (FAANG). It addresses specific challenges in building reliable, efficient, and scalable generative AI systems in production.

Product Design Questions is regularly tested in GenAI engineering interviews at OpenAI, Anthropic, Google DeepMind, and AI-forward product companies. Understanding the trade-offs and failure modes demonstrates production-level expertise.

Related Topics

These topics form the surrounding GenAI in Interviews (FAANG) ecosystem:

• GenAI System Design — System design interview answers build directly on the GenAI system design patterns from this course
• Evaluation and Benchmarks — Evaluation design is a standalone interview topic - know MMLU, HumanEval, Arena, and LLM-as-Judge trade-offs
• AI Safety and Alignment — Safety questions appear in every senior GenAI interview - understanding jailbreaks, guardrails, and red teaming is expected

Key Ideas

• **Architecture questions:** explain attention mechanism, why KV cache works, what makes transformer inference memory-bound vs compute-bound, trade-offs between MoE and dense models
• **Optimization questions:** given a 70B model and a 100ms latency budget, which optimizations to apply in order; quantization vs speculative decoding vs batching trade-offs
• **Evaluation questions:** design a benchmark for a specific task; handle contamination; distinguish capability from alignment; design human preference evaluation protocol
• **Product design questions:** when to use RAG vs fine-tuning, how to handle hallucinations in a medical application, how to build a cost-effective multi-tenant LLM API

Вопросы для размышления

• How does GenAI in Interviews (FAANG) change when moving from a prototype to a production system serving 1 million users?
• What are the primary failure modes in GenAI in Interviews (FAANG) and what monitoring catches them before users are affected?
• How would you explain the trade-offs in GenAI in Interviews (FAANG) to a non-technical stakeholder who needs to approve the infrastructure budget?

Связанные уроки

• gai-23 — Interviews drill GenAI system design deeply
• gai-21 — Evaluation questions are a staple of interviews
• aie-42-ai-system-design — Same design tradeoffs surface in interviews
• ml-55-ml-system-design — Mirrors classic ML system design interviews
• gai-22 — Safety and product questions appear in interviews
• ml-01-intro