Trying to take it. TikTok of Machine Learning Engineer Newcomer Post? Then you won't want to miss this TikTok VO The whole process of interview review! In this article, we will share the real questions, interview process, answer ideas and preparation suggestions in detail to help you better understand the TikTok interview style and improve your pass rate.
TikTok MLE VO: Code
Title:Given a string of numbers, find all possible legitimate IP addresses. Check that each segment is 1-3 bits long, has a value between 0-255, and has no leading zeros (unless it is a separate "0"). Use backtracking for efficient enumeration.
TikTok MLE VO: Models & Eight Units
The following configuration is based on the Transformer model and training details from a paper:
- model structure::
- 32 layers, 32 Attention heads (head size 64)
- Using Rotary Embedding (dim = 32), context length = 2048
- Memory and speed optimization based on Flash-Attention
- Training details::
- Random initialization, fixed learning rate (fixed lr)
- Weight decay = 0.1
- Adam optimizer (β1=0.9, β2=0.98, ε=1e-7)
- Using fp16 + DeepSpeed ZeRO Stage 2
- Batch size = 2048, total training 150B tokens
Typical Interview Questions
1. What is Multi-Head Attention?
- Head:A set of (Q,K,V) projections are learned separately for the inputs and different features are extracted.
- Multi-Head:Parallelize multiple Heads to capture dependencies from multiple perspectives.
- Other Attention variants:Cross-Attention, Sparse/Local Attention, Axial Attention, etc.
2. What is Rotary Embedding?
- Rotary PE:Embedding the position information in a rotation matrix into Q/K belongs to Positional Encoding (Positional Encoding).
- Other PE methods: Absolute PE (sine-cosine), Learnable PE, Relative PE.
- Advantage:Natural fusion of relative position information, better long range reasoning and generalization.
3. How to deal with long-distance contexts?
- Sparse/Sliding Window Attention
- Performer / Linformer (low rank approximation)
- Memory-based mechanisms (Longformer, BigBird)
- Chunking or Layering Attention
4. What is Flash-Attention?
Attention implementation that performs block-level computation on the GPU, reducing memory reads and writes, dramatically speeding up and reducing graphics memory footprint.
5. Why use Adam and what do β1, β2 and ε mean?
- Adam:Combines AdaGrad and RMSProp for fast convergence and robustness to hyperparameters.
- β1/β2: first and second order momentum decay; ε: numerical stabilization term; lr: learning rate.
6. Why use a fixed learning rate instead of a warm-up?
For mature initialization and hyperparameter configurations, fixed lr is stable enough to reduce warm-up complexity.
7. What is Weight Decay?
Adding an L2 regularization term to the parameter update helps prevent overfitting and improves generalization.
8. how does fp16 compare to other accuracies?
- fp16 (half-precision): accelerates training, saves video memory
- Others: fp32, fp64, bf16 (greater dynamic range)
9. What is DeepSpeed ZeRO Stage 2?
Decentralizes optimizer state to multiple GPUs, reducing graphics memory overhead on a single card for training larger models.
consultation
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