Researcher

we are seeking a senior AI Researcher to join its R&D group and lead the frontier of large-scale LLM optimization. You will focus on maximizing performance, scalability, and efficiency of LLM training and inference across massive GPU clusters, bridging deep learning research, distributed systems design, and hardware-aware optimization.
At our company, we treat AI performance as a systems problem. Just as we reinvented networking through disaggregation and software-defined scale, were applying the same philosophy to AI infrastructure. Your work will directly influence how large models are deployed, scaled, and optimized across high-density compute environments.
Key Responsibilities
● Conduct cutting-edge research in artificial intelligence and machine learning, from problem formulation to experimental validation.
● Research, design, implement and evaluate novel algorithms, models, optimization strategies and architectures across areas of large-scale LLM training and inference (e.g., tensor/pipeline/expert parallelisms, quantization, prefill/decode disaggregation, GPU communication optimization).
● Translate research ideas into working prototypes and production-ready solutions.
● Stay up to date with state-of-the-art research, frameworks, and emerging trends in the AI ecosystem.
● Publish research findings internally and externally (papers, technical reports, blog posts, or patents) and present results to internal and external technical audiences.
● Collaborate closely with engineers, product teams, and other researchers to align research with real- world impact
● Profile distributed training and inference pipelines - identifying algorithmic, memory, and scheduling inefficiencies to contribute to a technical decision-making and long-term research roadmaps.
● Validate research through measurable impact, higher throughput, better FLOPS utilization, improved convergence efficiency, or reduced compute cost.