How to Use the Company-Wise AI Engineering Interview Question Repository

What This Project Provides

AI Engineering Interview Questions Company Wise is a curated preparation resource containing publicly reported interview questions from 35 companies. Questions are organized both by recurring technical topic and by company, making it easier to identify common expectations and prepare for a specific interview loop.

The repository is relevant to roles including AI Engineer, Generative AI Engineer, LLM Engineer, Agentic AI Engineer, Machine Learning Engineer, Research Engineer, Applied Scientist, Forward Deployed Engineer, AI Solutions Architect, AI Platform Engineer, MLOps Engineer, and LLMOps Engineer.

The questions are compiled from public interview experiences. Interview processes vary by team, seniority, region, and time, so the collection should be treated as a preparation map rather than an exact script.

Key Features

• Company-wise organization: Browse questions associated with frontier AI labs, big technology companies, infrastructure providers, AI-native product companies, and enterprise AI organizations.
• Cross-company topic lists: Start with questions that recur across multiple employers instead of studying duplicate entries.
• Role coverage: Prepare for research, applied AI, platform, inference, agent, solutions, and operational roles.
• Reported interview context: Company sections describe covered roles, publicly reported interview loops, and company-specific topics.
• Linked explanations: Many questions include links to articles or videos containing an answer or deeper explanation.
• Ongoing updates: The maintainers state that they will continue adding questions and answers.

Topics Covered

The common-question section is divided into major AI engineering domains. Use these domains as the foundation of your study plan.

LLM Internals and Architecture

This section covers attention, KV caches, MQA, GQA, MLA, FlashAttention, tokenization, positional encoding, RoPE, scaling laws, mixture-of-experts models, normalization, transformer feed-forward networks, and decoding strategies.

Representative questions include:

• Why does scaled dot-product attention use the 1/sqrt(d_k) factor?
• What are the memory implications of a KV cache at scale?
• Why can FlashAttention be faster without reducing FLOPs?
• How do greedy decoding, beam search, top-k, top-p, and temperature sampling differ?
• What happens tensor by tensor during a decoder-only transformer forward pass?

Inference, Serving, and GPU Performance

This category focuses on deploying LLMs efficiently. It includes prefill and decode behavior, continuous batching, PagedAttention, speculative decoding, prompt caching, quantization, parallelism, latency metrics, GPU capacity estimates, and serving frameworks.

You should be prepared to discuss both definitions and operational trade-offs. For example, an interviewer may ask you to diagnose a doubled p99 latency after deployment or estimate the memory required to serve a 70-billion-parameter model.

RAG and Retrieval

The retrieval section covers chunking, sparse and dense retrieval, hybrid search, reranking, retrieval evaluation, HyDE, agentic RAG, approximate nearest-neighbor indexes, permission-aware retrieval, index freshness, document parsing, and citation attribution.

Agents and Tool Use

Agent-related questions examine ReAct, tool calling, MCP, retries, timeouts, tool schemas, multi-agent orchestration, memory, loop termination, human approval, auditability, and long-running agent drift.

Fine-Tuning and Alignment

This section includes RLHF, PPO, DPO, GRPO, LoRA, QLoRA, prompt and prefix tuning, catastrophic forgetting, RL with verifiable rewards, reward hacking, and knowledge distillation.

Evaluation and Observability

Evaluation questions address LLM-as-judge systems, limited ground truth, expensive expert labels, hallucination detection, and regression gates for deciding whether model or prompt changes should ship.

Access and Setup

This project is a documentation repository rather than a software library, so no package installation is required. You can read it directly on GitHub.

Option 1: Use It in the Browser

1. Open the repository on GitHub.
2. Use the table of contents to open Common Questions Asked Across Companies.
3. Select a technical category such as RAG, inference, or agents.
4. Follow linked answers when they are available.
5. Move to your target company's section after completing the common material.

Option 2: Clone It Locally

Cloning the repository provides an offline copy that you can search, annotate, and update.

git clone https://github.com/pallavi-shekhar/ai-engineering-interview-questions-company-wise.git
cd ai-engineering-interview-questions-company-wise

To retrieve future updates, run:

git pull

You can then open the README in a text editor or a Markdown viewer and use full-text search to find a company, technology, or keyword.

Basic Usage: Build a Preparation Plan

Step 1: Choose the Role You Are Targeting

Start by defining the role because different positions emphasize different parts of the repository. An LLM inference role may require deeper GPU and serving knowledge, while an applied AI role may focus more heavily on RAG, evaluation, and product trade-offs.

A simple role-to-topic mapping might look like this:

• LLM or inference engineer: Transformer internals, KV-cache memory, batching, quantization, parallelism, and throughput.
• Applied AI engineer: RAG, agents, evaluation, prompting, and fine-tuning decisions.
• Agent engineer: Tool schemas, retries, memory, loop termination, orchestration, and human approval.
• MLOps or LLMOps engineer: Serving metrics, observability, regression gates, capacity planning, and production diagnosis.

Step 2: Complete the Common Questions First

The README explicitly recommends beginning with questions that appear across companies. This gives you broad coverage and prevents repeated study.

For each question, prepare four layers of understanding:

1. Definition: Explain the concept clearly in one or two sentences.
2. Mechanism: Describe how it works internally.
3. Trade-offs: Explain benefits, costs, limitations, and failure modes.
4. Application: Give an example of when you would or would not use it.

Step 3: Use Linked Answers Selectively

Many entries include links to Outcome School articles or videos. Read the question before opening an answer and attempt a response from memory. Then use the linked material to identify missing details.

For example, the KV-cache question should lead you beyond a basic definition. Your final preparation should include the cache's purpose, its growth with sequence length, its memory implications, and the serving techniques used to manage it.

Step 4: Study the Target Company

After covering the common topics, open the section for the company you are targeting. The repository groups organizations into categories such as frontier AI labs, big technology companies, infrastructure companies, AI-native product companies, and enterprise AI companies.

Use the company section to identify:

• The roles represented by the reported interview experiences.
• The publicly reported structure of the interview loop.
• Questions or themes that are more specific to that organization.
• Technical areas that deserve additional practice.

Step 5: Convert Questions into Active Practice

Do not only read the entries. Answer them aloud, write short explanations, derive relevant formulas, and practice follow-up questions.

For a question about continuous batching, for example, use this answer structure:

1. Define static and continuous batching.
2. Explain why requests finish at different times.
3. Describe how freed batch slots can be reused.
4. Connect the mechanism to utilization and throughput.
5. Discuss scheduling, latency, and implementation trade-offs.

Worked Study Examples

Example 1: Preparing an Inference Question

Suppose you select the question asking why prefill is compute-bound while decoding is memory-bandwidth-bound.

1. Define the prefill and decode phases.
2. Explain how prompt tokens can be processed in parallel during prefill.
3. Explain why autoregressive decoding produces tokens sequentially.
4. Relate repeated weight access during decoding to memory bandwidth.
5. Connect the distinction to TTFT, per-token latency, batching, and serving architecture.
6. Read the linked prefill-versus-decode explanation and refine your answer.

Example 2: Preparing a RAG Design Question

For a question about choosing BM25, dense retrieval, or both, build your answer around requirements rather than naming a universally best retriever.

1. Identify the corpus, query types, latency target, and evaluation metric.
2. Discuss the strengths of lexical matching for exact terms.
3. Discuss semantic retrieval for conceptually related language.
4. Explain when hybrid search may be useful.
5. Describe how you would evaluate retrieval before evaluating generated answers.
6. Add operational concerns such as permissions, index freshness, and reranking cost.

Example 3: Preparing an Agent Reliability Question

For tool-call errors, timeouts, and retries, structure the answer as a production control loop:

Validate request
Call tool with timeout
Classify the failure
Retry only when appropriate
Apply retry and cost limits
Record the action and result
Escalate or stop safely

The repository's related questions can then extend the discussion into loop termination, human approval, reversible actions, auditability, and drift during long-running tasks.

Advanced Preparation Tips

Practice Derivations, Not Just Definitions

Several questions explicitly request formulas or numerical estimates. Practice calculating KV-cache memory, model-weight memory at different precisions, fine-tuning memory, and approximate serving capacity. State assumptions before calculating because results depend on architecture, precision, batch size, and sequence length.

Connect Concepts Across Categories

Strong interview answers often connect architecture to production behavior. For example:

• KV-cache design connects transformer attention to GPU memory and serving capacity.
• Quantization connects numeric precision to model quality, memory use, and throughput.
• Chunking connects document structure to retrieval recall and answer attribution.
• Tool schema design connects model behavior to agent reliability and safety.
• Fine-tuning choices connect data quality to cost, latency, and catastrophic forgetting.

Turn System Questions into Requirement Discussions

When asked to design an AI system, begin with constraints instead of immediately naming tools. Clarify expected traffic, latency, quality, data freshness, privacy, permissions, failure handling, and cost. Then explain how each architectural decision addresses those requirements.

Prepare Diagnostic Frameworks

Production interviews often use scenarios rather than factual questions. For a latency regression, separate the investigation into recent changes, traffic shape, queueing, prompt lengths, output lengths, batching, cache behavior, GPU utilization, memory pressure, network dependencies, and downstream services.

Maintain an Evidence-Based Tracker

Create a local checklist with a confidence score for every question:

0 - Unseen
1 - Recognize the topic
2 - Can explain the definition
3 - Can explain mechanism and trade-offs
4 - Can solve follow-ups or calculations
5 - Can teach it and handle design variants

Review low-scoring common questions before spending time on narrow company-specific material.

Run Mock Interviews by Company

Choose questions associated with the target employer and answer under time constraints. Ask a practice partner to challenge assumptions, request calculations, introduce failures, or change requirements. This better reflects an interview conversation than memorizing fixed responses.

Account for Repository Updates

The maintainers plan to add new questions and answers. If you cloned the repository, run git pull periodically. If you use the browser version, revisit the common-question and target-company sections before an interview.

Important Limitations

• Reported questions do not guarantee what a future interviewer will ask.
• Interview loops can change by team, level, location, and hiring period.
• Not every question currently has a linked answer.
• A linked explanation should support active practice, not replace it.
• Company association should be used to prioritize topics rather than memorize scripts.

Conclusion

This repository is most effective when used as a structured curriculum rather than a list to skim. Begin with recurring questions, deepen each answer through mechanisms and trade-offs, then study the relevant company section and practice realistic follow-ups. Combining the collection with derivations, design exercises, diagnostic scenarios, and mock interviews creates a focused preparation process for a wide range of AI engineering roles.