On-demand delivery is an emerging business in recent years where accurate indoor locations of Gig couriers play an important role in the order dispatch and delivery process. To cater to this need, WiFi-based indoor positioning methods have become an alternative method for on-demand delivery thanks to extensive WiFi deployment in the indoor environment. Existing WiFi-based indoor localization and positioning methods are not suitable for large-scale on-demand delivery scenarios due to high costs (e.g., high labor cost to collect fingerprints) and limited coverage due to limited labeled data. In this work, we explore (i) massive crowdsourced WiFi data collecting from wearable or mobile devices of couriers with little extra effort and (ii) natural manual reports data in the delivery process as two opportunities to perform merchant-level indoor positioning in a weak-supervised manner. Specifically, we proposed WePos, an end-to-end weak-supervised-based merchant-level positioning framework, which consists of the following three parts: (i) a Bidirectional Encoder Representations from Transformers (BERT) based pre-training module to learn latent embeddings of WiFi access points, (ii) a contrastive label self-generate module to produce pseudos for WiFi scanning lists by matching similarity embedding clustering results and couriers' reporting behaviors. (iii) a deep neural network-based classifier to fine-tune the whole training process and conduct online merchant-level position inference. To evaluate the performance of our system, we conduct extensive experiments in both a large-scale public crowdsourcing dataset with over 50 GB of WiFi signal records and a real-world WiFi crowdsourced dataset collected from Eleme, (i.e., one of the largest on-demand delivery platforms in China) in four multi-floor malls in Shanghai. Experimental results show that WePos outperforms state-of-the-art baselines in the merchant-level positioning performance, offer up to 91.4% in positioning accuracy.