Skill specialists
Hire PyTorch Engineers
Browse deep learning engineers and researchers who build, train, and deploy PyTorch models in production — from custom architectures to LoRA fine-tuning and ONNX export.
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Stijn
Teddy
Gulshat
Lahiru Nuwan
Michael
Hiring guide
Why companies hire PyTorch experts
PyTorch has become the default framework for deep learning research and production at leading AI labs and companies. Engineers with strong PyTorch skills can handle everything from custom model architecture to distributed training and ONNX export.
Popular projects
- Fine-tune an open-source LLM (LLaMA, Mistral) on your proprietary data
- Build a custom deep learning model for image, text, or audio
- Optimise and export a trained model for production inference
- Train a computer vision system for detection or segmentation
What to look for
- Deep understanding of autograd, tensors, and training loops
- Experience with Hugging Face Transformers integration
- Knowledge of distributed training (DDP, FSDP) for large models
Frequently asked questions
Why hire a PyTorch engineer specifically?
PyTorch engineers have specialised knowledge of deep learning internals — gradient computation, custom loss functions, training optimisation, and model debugging. This goes beyond general Python or ML skills.
Can PyTorch engineers also work with Hugging Face?
Yes. Most PyTorch engineers on RemoteAI are experienced with Hugging Face Transformers and Datasets, as the two are deeply integrated in modern LLM workflows.
What is the difference between a PyTorch engineer and an ML engineer?
An ML engineer handles the full lifecycle — from data to deployment. A PyTorch specialist goes deeper on model design, training, and framework-level optimisation. Many engineers on RemoteAI cover both.
Can a PyTorch engineer fine-tune LLMs like LLaMA or Mistral?
Yes. PyTorch engineers with Hugging Face experience regularly fine-tune open-source LLMs using LoRA, QLoRA, and full SFT. This is one of the most in-demand PyTorch use cases right now.
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