AI Software Engineer

AI Software Engineer Location: Penang, Malaysia Responsibilities: • Design, develop, train, and evaluate machine learning and deep learning models, including CNNs, Transformers, Diffusion Models, and other Generative AI architectures. • Research, prototype, and implement state-of-the-art AI algorithms, architectures, and training methodologies. • Conduct experiments, benchmark performance, and optimize models for accuracy, efficiency, scalability, and deployment requirements. • Develop AI training, validation, testing, and inference pipelines. • Deploy and optimize AI solutions for embedded and edge computing environments. • Develop embedded software components to support AI inference and system integration on FPGA-based platforms. • Analyse and optimize end-to-end AI system performance, considering latency, throughput, memory utilization, and power efficiency. • Collaborate with cross-functional engineering teams to integrate AI technologies into products and applications. • Stay up to date with advancements in Artificial Intelligence, Generative AI, Embedded AI, and Edge Computing technologies. Qualifications: • Bachelor's, Master's, or Ph.D. degree in Computer Science, Artificial Intelligence, Electrical Engineering, Computer Engineering, or a related field. • Strong understanding of machine learning, deep learning, and neural network architectures. • Experience developing AI and deep learning models using PyTorch and/or TensorFlow. • Strong programming skills in Python. • Experience with C/C++ development. • Knowledge of model optimization techniques, including quantization, pruning, and model compression. • Experience designing and optimizing CNN architectures. • Experience with Generative AI models, including Transformers, Large Language Models (LLMs), Variational Autoencoders (VAEs), or Diffusion Models. • Familiarity with embedded software development and embedded Linux environments is an advantage. • Knowledge of FPGA-based AI deployment and acceleration techniques is an advantage. • Experience with FPGA development platforms or toolchains is an advantage. • Strong analytical, problem-solving, and debugging skills. • Ability to work effectively in a collaborative engineering environment.