Deep Learning Engineer VA
Designs and trains neural networks for vision, speech, language and generative tasks, then optimises them to run efficiently in production. Suits businesses with image, audio, video or large text workloads that off-the-shelf tools cannot handle.
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- No lock-in contracts
- Replacement guarantee
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Hourly rates · No lock-in contracts
Entry Level
$15 / hr
Intermediate ★
$20 / hr
Senior
$28 / hr
Responsibilities
- Design and train neural network architectures (CNNs, transformers, diffusion models) with PyTorch or TensorFlow for the client's specific data.
- Build datasets, labelling workflows and augmentation pipelines that produce reliable training data.
- Fine-tune pretrained models from Hugging Face and other model hubs, including LLMs and vision transformers, using LoRA and similar techniques.
- Run distributed training on GPU clusters and manage compute cost across AWS, Azure, Google Cloud or on-premise hardware.
- Evaluate models rigorously with held-out sets, error analysis and bias checks, and report results honestly.
- Optimise inference with ONNX, TensorRT, quantisation and distillation to meet latency and cost targets.
- Deploy models behind APIs or on edge devices and monitor them in production.
- Track experiments with Weights & Biases or MLflow and keep every result reproducible.
- Read and apply current research where it solves a real problem, and explain it to the team in plain terms.
Software & Tools
Python
PyTorch
TensorFlow
Keras
CUDA
Hugging Face
Weights & Biases
Docker
Requirements
- Minimum 5 years in ML or software engineering with at least 3 years focused on deep learning in production.
- Expert PyTorch or TensorFlow, strong Python and CUDA-aware performance tuning.
- Hands-on experience fine-tuning transformer models, working with Hugging Face libraries and building computer vision or NLP systems.
- Experience with GPU infrastructure, distributed training and model optimisation for deployment.
- Strong mathematics: linear algebra, probability, optimisation and the ability to read research papers critically.
- Master's or PhD in computer science, machine learning or a related field, or an equivalent record of deployed deep learning work.
- Good spoken and written English for research write-ups and cross-team collaboration.
- Reliable high-speed internet with backup power, and availability across the client's core hours.
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