Multimodal Algorithm Researcher

2 weeks ago


Singapore Hireio, Inc. Full time
Hireio, Inc. Job DescriptionAbout the Team

Hireio, Inc.

The team at Hireio, Inc. utilizes the most advanced AI technology to combat various risks and violations in our e-commerce platform, maintain platform security, build a good e-commerce ecosystem, and empower business teams to improve work efficiency.

Our ultimate goal is to achieve the highest risk detection capability while ensuring the fairness and sustainability of the e-commerce ecosystem, high-quality content, and merchandise.

Responsibilities
  • Responsible for large-scale multimodal (vision, speech, natural language, etc.) algorithm research and implementation in e-commerce scenarios.
  • Responsible for pre-training and tuning multimodal models using massive e-commerce data.
  • Participate in the research and development of methods to use multimodal information more efficiently, digging in-depth into the valuable information contained in each modal in the e-commerce scenario, and fully understanding it in multiple dimensions.
  • Support the production of scalable and optimized AI/machine learning (ML) models.
  • Focus on building algorithms for the extraction, transformation, and loading of large volumes of real-time, unstructured data to deploy AI/ML solutions from theoretical data science models.
  • Run experiments to test the performance of deployed models, and identify and resolve bugs that arise in the process.
  • Work in a team setting and apply knowledge of statistics, scripting, and programming languages required by the firm.
  • Work with the relevant software platforms on which the models are deployed.
Requirements
  • Minimum 3 years of work experience in related fields.
  • Familiar with one or more deep learning frameworks such as TensorFlow, PyTorch.
  • Have a deep understanding of multimodal mainstream research directions (multimodal representation learning, modal transformation, alignment, multimodal fusion, co-learning).
  • Have deep understanding and project experience in NLP, entity mining, text classification, detection, segmentation, video classification, etc.
  • Candidates with practical experience in DALL-E, CLIP, GLIDE, and other related projects are preferred.
  • Candidates with good performance in international competitions in related fields (such as VQA, Image Captioning/Retrieval) are preferred.
  • Candidates with published papers in high-level computer science conferences (ACL, EMNLP, NIPS, AAAI, etc.) are preferred.


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