Deskripsi Pekerjaan
Are you ready to redefine the boundaries of artificial intelligence? Google Research is seeking an exceptional Staff Research Scientist to join our ML Efficiency team in Singapore. In this role, you will lead high-impact research initiatives focused on optimizing generative model architectures, pushing the limits of computational efficiency, and deploying state-of-the-art AI at a global scale.
As a Staff Research Scientist, you will sit at the intersection of deep learning theory and systems engineering. You will collaborate with world-class engineers and researchers to develop novel algorithms that reduce the latency and hardware footprint of large-scale generative models without sacrificing performance. This is a unique opportunity to shape the future of AI infrastructure while working on problems that directly impact billions of users worldwide.
Google offers an inclusive environment where curiosity is celebrated and innovation is a daily practice. Join us in building the next generation of efficient, scalable, and responsible AI.
Tanggung Jawab
- Architect and implement novel optimization techniques for large-scale generative models (LLMs, diffusion models).
- Lead technical research directions that bridge the gap between algorithmic innovation and hardware-aware implementation.
- Collaborate cross-functionally with product and engineering teams to translate research breakthroughs into production-grade solutions.
- Mentor junior researchers and engineers, fostering a culture of technical excellence and scientific integrity.
- Publish original research in top-tier conferences (NeurIPS, ICML, ICLR, etc.) and contribute to the internal Google AI ecosystem.
- Analyze and improve model training/inference throughput through innovative pruning, quantization, and distillation methods.
Kualifikasi
- PhD in Computer Science, Electrical Engineering, Mathematics, or a related field with a focus on Deep Learning.
- Extensive experience in research and development of deep learning architectures, specifically generative models.
- Strong track record of peer-reviewed publications in prestigious AI/ML conferences.
- Proficiency in Python and deep learning frameworks such as TensorFlow, JAX, or PyTorch.
- Proven ability to bridge theoretical machine learning research with practical systems optimization.
- Excellent communication skills with the ability to lead complex projects and influence stakeholders.
- Deep understanding of hardware acceleration (GPUs, TPUs) and its interaction with deep learning workflows.