Deskripsi Pekerjaan
Are you a visionary researcher passionate about bridging the gap between sound and semantics? The Institute for Infocomm Research (I2R) at the Agency for Science, Technology and Research (A*STAR) is seeking a Lead Research Engineer to drive innovation within our Aural & Language Intelligence department. In this pivotal role, you will lead high-impact research projects focused on next-generation audio AI, speech recognition, and multimodal intelligent systems.
As a Lead Research Engineer, you will operate at the intersection of deep learning and acoustic signal processing. You will have the opportunity to work with cutting-edge computational resources, collaborating with world-class scientists to transition laboratory research into practical, industry-leading applications. If you thrive in an environment that rewards intellectual curiosity and technical excellence, we invite you to help us shape the future of machine perception.
Tanggung Jawab
- Lead and manage end-to-end research projects in Aural AI, speech processing, and Natural Language Understanding.
- Architect and implement robust deep learning models for complex multimodal data environments.
- Collaborate with cross-functional teams to design scalable AI solutions for real-world application.
- Mentor junior researchers and engineers, fostering a culture of technical rigor and innovation.
- Analyze performance metrics of current audio-language models and implement state-of-the-art optimizations.
- Publish findings in top-tier conferences (e.g., ICASSP, NeurIPS, Interspeech) and represent I2R at international forums.
- Bridge the gap between theoretical research and prototype deployment for industry partners.
Kualifikasi
- Master’s or PhD degree in Computer Science, Electrical Engineering, or a related quantitative discipline.
- Proven track record (5+ years) in deep learning, audio signal processing, or computational linguistics.
- Expertise in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Solid understanding of Transformer architectures, GANs, or multimodal integration techniques.
- Experience in deploying machine learning models in production environments is highly preferred.
- Strong problem-solving skills and the ability to articulate complex technical ideas to diverse stakeholders.
- Excellent communication and leadership capabilities to spearhead complex engineering initiatives.