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Design and optimize communication stacks and runtime for large-scale distributed training (including MoE and hybrid parallelism). Improve collective performance, reduce latency, ensure fault-tolerant execution, debug NCCL/UCX/RDMA issues, and co-design topology-aware orchestration for 1,000+ GPU clusters.

Develop and maintain full-stack applications supporting foundation-model research. Design client- and server-side architecture, ensure responsiveness, security, and performance, collaborate with researchers/ML engineers, troubleshoot and document software, and contribute to HPC and deep-learning research outputs and publications.

Develop and implement large-scale machine learning and foundation-model systems, collaborate with research teams to integrate technologies, maintain ML lifecycle (data preprocessing, pre-/post-training, evaluation), conduct design reviews and code reviews, debug system issues, contribute to documentation and research publications, and represent the institute at conferences.

Short-term, intensive internship supporting foundation model work: prepare datasets, assist with model architecture and testing, analyze outputs for accuracy and bias, refine algorithms, and document findings for research collaboration.

Design, develop, and operate high-performance distributed software for large-scale AI/ML training. Optimize kernel and accelerator code, tune communication libraries (NCCL, MPI, UCX, RCCL, RDMA), support ML frameworks (PyTorch, MegatronLM, DeepSpeed), and contribute to scheduling/orchestration (Slurm, Kubernetes). Collaborate with researchers, hardware vendors, and open-source communities to debug and improve performance across the stack.

Research and engineer agentic language models: develop algorithms, datasets, evaluations, and scalable RL and distributed training/serving infrastructure; publish papers and contribute open-source software.

Lead research on efficient large language models and multimodal integration. Design, implement, and optimize NLP algorithms and training; collaborate across teams, write technical reports and papers, and represent the institute at conferences.

Build and maintain scalable multimodal (primarily video) data pipelines, support large-scale distributed training and inference optimization for world and video generative models, integrate research prototypes into production, ensure system reliability, and contribute to evaluation, documentation, and research outputs.

Build and grow a developer and user community around IFM foundation models; run events and trainings; translate research into docs, demos, and onboarding; support external adopters and gather feedback to inform product and research roadmaps.

Build and maintain large-scale data collection and preprocessing pipelines for NLP research. Develop web crawlers, APIs, and workflows; refine LLM outputs into structured datasets; collaborate with researchers to ensure data quality and document methodologies. Support scalable storage, retrieval, and distribution of datasets.