Worked on the Nixtla/neuralforecast repository to enhance the efficiency of attention mechanisms in deep learning models. Integrated Flash Attention into the attention stack, modifying both FullAttention and _ScaledDotProductAttention layers to accelerate attention computations for longer sequences. Implemented a robust fallback to PyTorch’s scaled_dot_product_attention, ensuring compatibility when Flash Attention is unavailable or does not output attention weights. This approach reduced computational costs and improved forecasting speed. The work demonstrated strong proficiency in Python, PyTorch, and transformer models, with a focus on performance optimization and maintainable code. Delivered the feature within a month, emphasizing depth in engineering and technical execution.