This work demonstrates that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the traditional sequential optimization of reconstruction and analysis components. Instead, a more global gradient-based optimization strategy is proposed, inspired by modern machine learning workflows with foundation models. The approach connects HEP reconstruction and analysis to concepts like pretraining, finetuning, domain adaptation, and high-dimensional embeddings, and quantifies performance improvements in the context of searching for heavy resonances decaying via an intermediate di-Higgs system to four b-jets. The paper outlines a conceptual correspondence between HEP analysis and modern deep learning, highlighting similarities in the use of foundation models, downstream tasks, and finetuning. It introduces a demonstrator use-case for end-to-end optimization, discussing datasets and neural network architectures. The study evaluates three architectures and three training strategies, comparing performance in terms of background rejection and data efficiency. Results show that finetuning significantly improves performance over a frozen backbone, with pretraining outperforming training from scratch. The study also explores domain adaptation, showing that pretraining on datasets other than the target dataset can improve performance. Key findings include the effectiveness of finetuning in improving performance and data efficiency, the benefits of pretraining over training from scratch, and the potential of domain adaptation in HEP. The paper concludes that joint optimization of reconstruction and analysis is beneficial, and that foundation models can be adapted to HEP tasks, leading to improved performance and data efficiency. The study also identifies important research questions, such as the integration of calibration techniques and the design of better pretraining tasks.This work demonstrates that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the traditional sequential optimization of reconstruction and analysis components. Instead, a more global gradient-based optimization strategy is proposed, inspired by modern machine learning workflows with foundation models. The approach connects HEP reconstruction and analysis to concepts like pretraining, finetuning, domain adaptation, and high-dimensional embeddings, and quantifies performance improvements in the context of searching for heavy resonances decaying via an intermediate di-Higgs system to four b-jets. The paper outlines a conceptual correspondence between HEP analysis and modern deep learning, highlighting similarities in the use of foundation models, downstream tasks, and finetuning. It introduces a demonstrator use-case for end-to-end optimization, discussing datasets and neural network architectures. The study evaluates three architectures and three training strategies, comparing performance in terms of background rejection and data efficiency. Results show that finetuning significantly improves performance over a frozen backbone, with pretraining outperforming training from scratch. The study also explores domain adaptation, showing that pretraining on datasets other than the target dataset can improve performance. Key findings include the effectiveness of finetuning in improving performance and data efficiency, the benefits of pretraining over training from scratch, and the potential of domain adaptation in HEP. The paper concludes that joint optimization of reconstruction and analysis is beneficial, and that foundation models can be adapted to HEP tasks, leading to improved performance and data efficiency. The study also identifies important research questions, such as the integration of calibration techniques and the design of better pretraining tasks.