Skill Profile

PyTorch

PyTorch: tensors, autograd, DataLoader, custom models, distributed training

Machine Learning & AI Deep Learning

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 6 optional

Domain

Machine Learning & AI

Group

Deep Learning

Last updated

3/17/2026

How to Use

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
Computer Vision Engineer		Builds basic CNNs in PyTorch for image classification using torchvision pretrained models (ResNet, VGG). Writes custom Dataset and DataLoader for image data. Applies torchvision.transforms for augmentation: random crop, flip, color jitter. Understands training loop basics.
Data Scientist		Builds simple neural networks in PyTorch for tabular data. Uses nn.Module for model definition. Understands tensor operations, autograd and basic training loop. Experiments with loss functions and optimizers (Adam, SGD). Tracks metrics across experiments.
LLM Engineer		Understands PyTorch tensor operations and autograd for transformer models. Uses HuggingFace Transformers with PyTorch backend for inference. Loads pretrained LLMs, runs tokenization and text generation. Grasps attention mechanism basics in PyTorch.
ML Engineer	Required	Trains neural networks in PyTorch using nn.Module. Writes training loops with DataLoader, loss computation, backpropagation and optimizer steps. Understands tensor shapes, device management (CPU/GPU) and model checkpointing. Uses torchmetrics for evaluation.
NLP Engineer	Required	Knows PyTorch basics for NLP: tensors, autograd, nn.Module. Trains simple NLP models: text classification via LSTM, embedding layers for word representations. Understands training loop.

Role	Required	Description
Computer Vision Engineer		Implements detection (YOLO, SSD) and segmentation (U-Net, Mask R-CNN) in PyTorch. Fine-tunes pretrained models with custom heads and layer freezing. Builds augmentation pipelines with Albumentations. Processes video with efficient batching and temporal models.
Data Scientist		Designs custom architectures with nn.Module for complex experiments. Uses PyTorch Lightning for structured training with callbacks and early stopping. Tunes hyperparameters with Optuna integration. Implements custom loss functions and LR schedulers. Profiles training bottlenecks.
LLM Engineer		Fine-tunes transformers with PyTorch using PEFT: LoRA, QLoRA, prefix tuning. Implements custom training loops for causal LM and seq2seq tasks. Works with tokenizers, attention masks and padding strategies. Integrates HuggingFace Trainer with DeepSpeed for efficient fine-tuning.
ML Engineer	Required	Designs custom models in PyTorch. Configures training loop: optimizer, scheduler, early stopping. Uses transfer learning (fine-tuning pretrained models). Logs experiments in MLflow/W&B.
NLP Engineer	Required	Independently develops NLP models with PyTorch: fine-tuning transformers, custom loss functions for NLP tasks, data loaders for text corpora. Uses mixed precision training.

Role	Required	Description
Computer Vision Engineer	Required	Designs end-to-end CV systems in PyTorch: multi-task learning, knowledge distillation, custom losses for detection/segmentation. Optimizes inference with TorchScript, quantization and pruning. Builds real-time video pipelines with efficient GPU memory management.
Data Scientist	Required	Architects complex PyTorch training with distributed data parallelism. Implements custom autograd functions for research. Leads architecture decisions: attention mechanisms, residual connections, normalization. Mentors team on PyTorch debugging and profiling with torch.profiler.
LLM Engineer	Required	Architects LLM training infrastructure in PyTorch. Implements custom attention, positional encodings and model parallelism. Optimizes memory with gradient checkpointing, mixed precision and Flash Attention. Designs evaluation frameworks for LM quality. Mentors team on transformer internals.
ML Engineer	Required	Designs custom architectures and training frameworks. Optimizes inference: ONNX export, TensorRT. Configures distributed training (DDP, FSDP). Works with PyTorch Lightning for production training.
NLP Engineer	Required	Designs complex NLP architectures with PyTorch: multi-task learning, knowledge distillation, model compression. Optimizes training through distributed training, gradient accumulation.

Role	Required	Description
Computer Vision Engineer	Required	Defines PyTorch strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Data Scientist	Required	Defines PyTorch standards for DS team: experiment structure, reproducibility requirements, model versioning. Evaluates ecosystem tools (Lightning, TorchRec) for team adoption. Reviews architectural decisions in training pipelines. Drives knowledge sharing on advanced patterns.
LLM Engineer	Required	Defines PyTorch-based LLM training standards: PEFT strategy selection, distributed training configs, evaluation benchmarks. Evaluates tools (vLLM, TensorRT-LLM) for production inference. Reviews fine-tuning architectures. Establishes best practices for reproducible LLM experiments.
ML Engineer	Required	Defines deep learning strategy for the organization. Designs training infrastructure. Standardizes training patterns and evaluation. Coordinates GPU resources.
NLP Engineer	Required	Defines PyTorch development standards for the NLP team. Establishes training best practices, model architecture guidelines, and ensures NLP experiment reproducibility.

Role	Required	Description
Computer Vision Engineer	Required	Defines PyTorch strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.
Data Scientist	Required	Shapes org-wide PyTorch strategy: version governance, TensorFlow migration, GPU cluster policies. Drives adoption of torch.compile and torch.export across teams. Defines cross-team standards for training infrastructure and model deployment. Influences ecosystem through contributions.
LLM Engineer	Required	Shapes org-wide LLM training platform on PyTorch: multi-team GPU allocation, model parallelism standards, pre-training vs fine-tuning investment. Drives PyTorch 2.x compiler adoption for LLM workloads. Defines cross-team standards for model artifacts and cost optimization.
ML Engineer	Required	Defines deep learning strategy for enterprise. Researches novel architectures. Optimizes GPU infrastructure costs. Publishes results.
NLP Engineer	Required	Shapes enterprise PyTorch strategy for the NLP platform. Defines model development standards, training infrastructure, and research-to-production pipeline at organizational level.

Junior 5 requirements

Computer Vision Engineer

Builds basic CNNs in PyTorch for image classification using torchvision pretrained models (ResNet, VGG). Writes custom Dataset and DataLoader for image data. Applies torchvision.transforms for augmentation: random crop, flip, color jitter. Understands training loop basics.
Data Scientist

Builds simple neural networks in PyTorch for tabular data. Uses nn.Module for model definition. Understands tensor operations, autograd and basic training loop. Experiments with loss functions and optimizers (Adam, SGD). Tracks metrics across experiments.
LLM Engineer

Understands PyTorch tensor operations and autograd for transformer models. Uses HuggingFace Transformers with PyTorch backend for inference. Loads pretrained LLMs, runs tokenization and text generation. Grasps attention mechanism basics in PyTorch.

Middle 5 requirements

Computer Vision Engineer

Implements detection (YOLO, SSD) and segmentation (U-Net, Mask R-CNN) in PyTorch. Fine-tunes pretrained models with custom heads and layer freezing. Builds augmentation pipelines with Albumentations. Processes video with efficient batching and temporal models.
Data Scientist

Designs custom architectures with nn.Module for complex experiments. Uses PyTorch Lightning for structured training with callbacks and early stopping. Tunes hyperparameters with Optuna integration. Implements custom loss functions and LR schedulers. Profiles training bottlenecks.
LLM Engineer

Fine-tunes transformers with PyTorch using PEFT: LoRA, QLoRA, prefix tuning. Implements custom training loops for causal LM and seq2seq tasks. Works with tokenizers, attention masks and padding strategies. Integrates HuggingFace Trainer with DeepSpeed for efficient fine-tuning.

Senior 5 requirements

Computer Vision Engineer
Required

Designs end-to-end CV systems in PyTorch: multi-task learning, knowledge distillation, custom losses for detection/segmentation. Optimizes inference with TorchScript, quantization and pruning. Builds real-time video pipelines with efficient GPU memory management.
Data Scientist
Required

Architects complex PyTorch training with distributed data parallelism. Implements custom autograd functions for research. Leads architecture decisions: attention mechanisms, residual connections, normalization. Mentors team on PyTorch debugging and profiling with torch.profiler.
LLM Engineer
Required

Architects LLM training infrastructure in PyTorch. Implements custom attention, positional encodings and model parallelism. Optimizes memory with gradient checkpointing, mixed precision and Flash Attention. Designs evaluation frameworks for LM quality. Mentors team on transformer internals.

Lead / Staff 5 requirements

Computer Vision Engineer
Required

Defines PyTorch strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Data Scientist
Required

Defines PyTorch standards for DS team: experiment structure, reproducibility requirements, model versioning. Evaluates ecosystem tools (Lightning, TorchRec) for team adoption. Reviews architectural decisions in training pipelines. Drives knowledge sharing on advanced patterns.
LLM Engineer
Required

Defines PyTorch-based LLM training standards: PEFT strategy selection, distributed training configs, evaluation benchmarks. Evaluates tools (vLLM, TensorRT-LLM) for production inference. Reviews fine-tuning architectures. Establishes best practices for reproducible LLM experiments.

Principal 5 requirements

Computer Vision Engineer
Required

Defines PyTorch strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.
Data Scientist
Required

Shapes org-wide PyTorch strategy: version governance, TensorFlow migration, GPU cluster policies. Drives adoption of torch.compile and torch.export across teams. Defines cross-team standards for training infrastructure and model deployment. Influences ecosystem through contributions.
LLM Engineer
Required

Shapes org-wide LLM training platform on PyTorch: multi-team GPU allocation, model parallelism standards, pre-training vs fine-tuning investment. Drives PyTorch 2.x compiler adoption for LLM workloads. Defines cross-team standards for model artifacts and cost optimization.

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