Hire PyTorch Developers
Hire pre-vetted PyTorch engineers from Latin America. LLM fine-tuning, computer vision, distributed training. 7-day match, 30–50% below US rates.
Top 1%
talent accepted
7 days
to first profiles
30–50%
below US rates
100%
timezone overlap
clients backed by
What is PyTorch and why do companies need PyTorch developers?
If you're training models, fine-tuning LLMs, or building production ML systems, PyTorch is almost certainly what your team runs. It's the framework behind Llama, Mistral, Stable Diffusion, and the majority of serious ML research that makes it to production. The hard part isn't knowing PyTorch — it's finding engineers who've actually debugged distributed training jobs, optimized CUDA memory, and shipped models to prod without blowing up latency. NeuronHire's LATAM engineers are vetted specifically on those depth areas: PEFT, LoRA, FSDP, torch.compile, and TorchServe. First profiles in 7 days, 30–50% below US market rates.
Built with PyTorch
What companies build with PyTorch
LLM fine-tuning and custom model training
Most teams fine-tuning Llama, Mistral, or Falcon are doing it with PyTorch, PEFT, and Hugging Face. You need engineers who know the difference between LoRA and QLoRA, when to use FSDP vs. DDP, and how to set up a training run that doesn't OOM at batch 2.
Computer vision systems
Whether it's YOLO for defect detection, SAM for segmentation, or a custom CNN for medical imaging, PyTorch is the standard. Engineers in this space need solid torchvision fundamentals and real experience deploying inference pipelines at speed.
Research-to-production ML pipelines
PyTorch's flexible eager execution gets models out of notebooks fast — but moving to production takes more: torch.compile for speed, TorchServe or Triton for serving, and ONNX export for edge cases. These are the gaps where most ML teams struggle.
The Process
Hire in 4 simple steps
From first call to signed developer in as little as two weeks.
Book a Call
A 30-minute discovery call where we understand your stack, team size, seniority needs, and timeline.
Get Matched
Within 7 days we deliver 2–3 hand-picked developer profiles from our vetted LATAM talent network.
Interview
You run your own technical interviews. We coordinate scheduling and give you our vetting notes to guide the conversation.
Hire
Select your developer, sign a flexible engagement agreement, and fast onboard
HOW WE VET DEVELOPERS
How we rigorously choose before you ever see them
From code quality to communication style, every candidate goes through a multi-layered process designed to ensure technical excellence and cultural alignment.
Profile Review
We verify experience, outcomes, and seniority. Only proven professionals move forward.
Soft Skills & Collaboration
We assess communication, collaboration, and English, no multiple-choice fluff.
Technical Evaluation
We test critical thinking and culture fit with real-world engineering challenges.
Precision Matching
Only aligned talent reaches you, by skills, timezone, and team style.
Related PyTorch skills we assess
These are the specific tools, libraries, and patterns every candidate is tested on before they reach you.
Use these to screen candidates
PyTorch interview questions
- 01Walk me through the difference between a tensor and a NumPy array — when would you use each?
- 02How do you define a simple feedforward network in PyTorch? What does the forward() method do?
- 03What is autograd and how does PyTorch use it during backpropagation?
- 01You're training a model and loss stops improving after epoch 3. Walk me through how you'd diagnose and fix it.
- 02Explain the difference between DataParallel and DistributedDataParallel. When does the performance trade-off favor DDP?
- 03You need to fine-tune a 7B parameter model on a single A100. What techniques would you use to fit it in memory and keep training stable?
- 01Walk me through how you'd design a production LLM fine-tuning pipeline — from raw dataset to deployed endpoint — including how you'd handle checkpointing, evaluation, and rollback.
- 02Your team's FSDP training run is hitting severe slow-downs at the communication step. How do you diagnose whether it's a network bottleneck, a gradient synchronization issue, or a data loading problem?
- 03How would you decide between exporting a model to ONNX, using TorchServe, or building a custom FastAPI inference server? What factors drive that call in a production system?
FAQ
PyTorch Developer FAQ
Common questions about hiring PyTorch developers from Latin America through NeuronHire.
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