Advanced 25 Questions • ~8 hrs study

AI Companies (OpenAI, NVIDIA, DeepMind)

Interview preparation for top AI companies — expect ML theory deep-dives, research paper discussions, ML coding challenges, and system design for AI.

1

What does an OpenAI interview look like?

Short Answer

OpenAI interviews: Recruiter → Technical phone screen (ML + coding) → Onsite (4-5 rounds: ML theory, coding, research discussion, system design for ML, culture fit). They expect deep understanding of transformers, RLHF, and scaling laws.

Key Focus Areas

  • Transformers: Attention mechanism, positional encoding, scaling
  • RLHF: Reward modeling, PPO, DPO, alignment techniques
  • Scaling Laws: Chinchilla optimal, compute vs data vs parameters
  • Safety & Alignment: Red teaming, capability vs alignment

💡 Memory Trick: "OpenAI = Deep ML theory + Research taste + Safety awareness + Coding ability"

2

How to prepare for NVIDIA ML/AI roles?

Short Answer

NVIDIA focuses on GPU computing, CUDA optimization, and deep learning infrastructure. Expect: C++/CUDA coding, ML theory, performance optimization, and understanding of GPU architecture.

Key Topics

  • GPU Architecture: CUDA cores, memory hierarchy, tensor cores
  • Performance: Mixed precision, kernel fusion, memory coalescing
  • Frameworks: TensorRT, Triton Inference Server, cuDNN
  • DL Training: Distributed training, gradient accumulation, model parallelism

💡 Memory Trick: "NVIDIA = Hardware-aware ML. Know your GPU memory, parallelism, and optimization"

3

What research papers should I know for AI company interviews?

Must-Know Papers

  • Attention Is All You Need (2017) — Transformer architecture
  • BERT (2018) — Bidirectional pre-training
  • GPT Series — Scaling autoregressive LMs
  • InstructGPT / RLHF (2022) — Alignment via human feedback
  • Scaling Laws (Kaplan 2020, Chinchilla 2022) — Compute optimal training
  • LoRA (2021) — Efficient fine-tuning

💡 Memory Trick: "Know the lineage: Transformer → BERT/GPT → Scaling → RLHF → Efficient Methods (LoRA)"

Questions 4-25 continue with AI company specifics.

Topics covered: DeepMind (research focus, math-heavy), Anthropic (safety-first, Constitutional AI), ML system design for AI companies, take-home ML challenges, presenting your research, and how to stand out as a candidate.

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