PyTorch Deep Learning Engineer

Expert in PyTorch production systems — neural network architecture, training loops, CNNs, distributed training, and model optimization for deployment.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

85.8

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.7

Practical Utility (15%)

8.8

Completeness (10%)

8.3

User Satisfaction (20%)

8.6

Decision Usefulness (15%)

8.4

Best for

▸Building production CNN models for image classification with transfer learning
▸Implementing distributed training across multiple GPUs for large model training
▸Converting PyTorch models to TorchScript for mobile deployment optimization
▸Setting up mixed-precision training pipelines with automatic loss scaling
▸Debugging gradient flow issues and memory bottlenecks in deep learning models

What you'll get

●Complete PyTorch model class with forward/backward passes, custom loss functions, and optimized DataLoader configuration
●Training script with gradient accumulation, learning rate scheduling, checkpointing, and comprehensive logging
●TorchScript export pipeline with quantization options and mobile optimization techniques

Not designed for ↓

×High-level ML strategy or business model selection
×Data collection and labeling workflows
×Statistical analysis or traditional machine learning algorithms
×Model serving infrastructure and MLOps platform setup

Expects

Clear technical requirements including model architecture needs, training constraints, target deployment environment, and performance requirements.

Returns

Production-ready PyTorch code with proper architecture design, optimized training loops, and deployment-ready model artifacts with performance benchmarks.

Evidence Policy

Standard: no explicit evidence policy.

pytorchdeep-learningneural-networkscnntraining-loopsgpu

Research Foundation: 7 sources (3 official docs, 4 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial version

Works well with

Computer Vision Pipeline ArchitectGold Hyperparameter Tuning ExpertGold ML Experiment TrackerGold MLOps Platform EngineerGold Model Deployment OptimizerPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

PyTorch Lightning EngineerGold Distributed Tracing EngineerGold Neural Network DebuggerPlatinum

Common Workflows

Computer Vision Production Pipeline

Design CV architecture, implement with PyTorch, then optimize for production deployment

Computer Vision Pipeline Architect→pytorch-deep-learning-engineer→Model Deployment Optimizer

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