Skill specialists

Hire Vetted 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.

Manually reviewed
Remote worldwide
All engagement types
Deep Learning Engineer LLM Fine-Tuning Engineer Computer Vision Engineer ML Research Engineer ML Systems Engineer

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Stijn

Stijn

Junior
Netherlands — Amsterdam (UTC+2) Contract, Freelance
AI & Machine Learning Engineers data science & analytics
python javascript sql r ai +5
Viren

Viren

Junior
India — Kolkata (UTC+5.5) Full Time, Part Time
AI & Machine Learning Engineers data science & analytics
Jesper

Jesper

Junior
Netherlands — Amsterdam (UTC+2) Full-time, Part-time
AI & Machine Learning Engineers NLP & Prompt Engineering
Andreas

Andreas

Junior
Cyprus — Nicosia (UTC+3) Full-time, Part-time
AI & Machine Learning Engineers NLP & Prompt Engineering
Yuwetha

Yuwetha

Junior
Sri Lanka — Colombo (UTC+5.5) Part-time, Full-time
NLP & Prompt Engineering AI Business, Marketing & Strategy
Sandeep

Sandeep

Junior
India — Kolkata (UTC+5.5) Full-time
AI & Machine Learning Engineers
Yash

Yash

Junior
India — Kolkata (UTC+5.5) Full-time, Part-time
AI & Machine Learning Engineers data science & analytics
Gautham

Gautham

Junior
India — Kolkata (UTC+5.5) Full-time, Part-time
AI & Machine Learning Engineers AI Implementation & Consulting
Nada

Nada

Junior
Egypt — Cairo (UTC+3) Contract-to-hire, Full-time
AI & Machine Learning Engineers data science & analytics
Muhammad Ibrahim

Muhammad Ibrahim

Junior
Pakistan — Karachi (UTC+5) Full-time
AI & Machine Learning Engineers NLP & Prompt Engineering

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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