You will work with large datasets, cutting-edge architectures, and GPU-accelerated training pipelines to build scalable, production-ready deep learning solutions.
Responsibilities Design, train, and optimise deep learning models for tasks such as NLP, computer vision, or generative AI. Develop high-performance training pipelines, including data pre-processing, augmentation, and batching. Conduct experiments, evaluate model performance, and iterate using metrics and validation frameworks. Deploy models into production systems using MLOps best practices.
Requirements Strong experience with deep learning frameworks (PyTorch, Tensor Flow, JAX). Proficiency in Python and experience with scientific computing libraries (Num Py, Pandas). Experience training and optimising neural networks, including CNNs, RNNs, Transformers, or diffusion models. Hands-on experience with GPUs, CUDA, or distributed training. Familiarity with data pipelines and large-scale data processing. Understanding of model evaluation, hyperparameter tuning, and experiment tracking.