Lilly Tech Systems
PyTorch Coding Challenges
The PyTorch challenges that NVIDIA, Meta, Google DeepMind, and OpenAI ask in deep learning engineer interviews. Each problem tests real skills: implementing custom layers from scratch, writing correct training loops, building loss functions, and debugging models. No toy examples — these are the problems that separate senior DL engineers from everyone else.
Your Learning Path
Follow these lessons in order for complete preparation for PyTorch-based DL coding interviews, or jump to any topic.
1. PyTorch in Coding Interviews
What to expect in DL interviews, tensor basics review, autograd fundamentals, and how top AI companies evaluate PyTorch fluency.
2. Tensor Operations
6 challenges: reshaping and views, broadcasting, advanced indexing, einsum, gradient computation, and device management.
3. Custom Layers & Modules
5 challenges: linear layer from scratch, multi-head attention, layer normalization, residual block, and positional encoding.
4. Training Loops
5 challenges: complete training loop, learning rate scheduling, gradient clipping, mixed precision training, and model checkpointing.
5. Custom Loss Functions
5 challenges: focal loss, triplet loss, contrastive loss, dice loss, and custom regularization terms.
6. Datasets & DataLoaders
5 challenges: custom dataset class, data augmentation pipeline, collate functions, distributed sampling, and streaming dataset.
7. Debugging & Optimization
5 challenges: finding bugs in model code, memory optimization, profiling, gradient checking, and NaN detection.
8. Patterns & Tips
PyTorch idioms, common pitfalls, production patterns, and frequently asked questions with detailed answers.
What You'll Learn
By the end of this course, you will be able to:
Build Custom nn.Modules
Implement attention heads, normalization layers, and residual blocks from scratch. This is the most common DL interview task.
Write Production Training Loops
Build training loops with mixed precision, gradient clipping, LR scheduling, and checkpointing — the way real teams ship models.
Implement Custom Losses
Write focal loss, triplet loss, contrastive loss, and dice loss from scratch. Know when and why to use each one.
Debug and Optimize Models
Find bugs in training code, fix memory leaks, profile bottlenecks, and detect NaN gradients — the skills that save teams days of debugging.