What Big‑Model Companies Really Look for: Experience, Papers, and Potential

Interview Evaluation Criteria for Large-Model Positions

Interviewers prioritize depth of understanding over superficial large-model experience. Only experience from core teams of leading model companies is considered highly relevant.

Common Pitfalls

Claiming large-model training experience without being able to explain the parallelism configuration (data parallelism DP, pipeline parallelism PP, tensor parallelism TP) or the meaning of key metrics such as MFU.

Inability to interpret Megatron‑LM launch arguments or to differentiate DP from DDP.

Relying solely on fine-tuning a LLaMA‑7B or similar “resume filler” without deeper system knowledge.

Valuable Technical Foundations

Strong academic fundamentals: calculus, linear algebra, statistics, and algorithmic problem solving (e.g., LeetCode).

Clear understanding of transformer architecture, tokenization schemes, and OS concepts (process vs thread).

Proficiency in multiple programming languages and ability to develop custom operators (e.g., using Triton).

Demonstrable High-Impact Experience

Implemented and benchmarked different pipeline algorithms on two 2080 Ti GPUs, reporting throughput and scaling behavior.

Developed custom GPU kernels or operators with Triton, measuring performance gains over native PyTorch ops.

Articulated differences among tokenizers used by various large models (e.g., BPE vs. SentencePiece vs. Unigram).

Contributed to open-source projects in languages other than Python, showing cross-language development competence.

Built a high-performing Gomoku AI, preferably using reinforcement‑learning techniques, and evaluated its win rate against baseline agents.

Potential Indicators

Interviewers assess two core attributes:

Foundation : solid academic background, ability to solve math and coding problems, and deep knowledge of model internals.

Curiosity : proactive study of recent papers, exploration of model weights, quantization methods, and continuous self‑learning even without direct training experience.

Reference Note

Candidates who received a Megatron launch command from a senior colleague but cannot explain each parameter are typically penalized.