Artificial Intelligence (AI) Researcher - Research Engineer / Research Fellow in Multimodal AI - WS1A

As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry in its research pursuits. This position is situated within the SIT x NVIDIA AI Centre (SNAIC).  This role is part of an industry innovation project with a large consumer goods company, where you will develop AI solutions, specifically vision-language models (VLM), and a corresponding evaluation framework applied to the personal care sector. The research focuses on fine-grained VLM capabilities such as spatial reasoning, temporal grounding, event tracking, and domain knowledge using a curated multimodal dataset. Key Responsibilities Lead the design, development, and evaluation of advanced AI solutions using vision-language model (VLM) to address research and industry use cases. Develop and implement data analytics methodologies, AI models, algorithms, and evaluation frameworks from prototyping through testing and validation. Apply statistical and analytical techniques to solve complex AI research challenges. Extract, integrate, analyse, and visualise complex multimodal data, and build datasets, benchmarks, and model evaluation pipelines to support research objectives. Utilise AI computing environments and software platforms to support model training and performance optimisation. Conduct model testing, benchmarking, and failure-mode analysis; interpret results and recommend improvements for scalability and deployment. Collaborate with the Principal Investigator, industry partners, and multidisciplinary teams to deliver project outcomes, technical reports, and publications. Manage the research project together with the Principal Investigator (PI) and industry partner to ensure all project deliverables are met. Mentor student assistants as appropriate. Requirements PhD or Masters degree in Computer Science or related field Expertise in multimodal AI, in particular computer vision and vision-language models Experience in developing, testing, validating, and benchmarking machine learning and machine learning models. Proficiency in Python programming and deep learning frameworks (e.g., PyTorch) Interest in multi-disciplinary, applied, industry-collaborative research