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Conduct and prototype novel reinforcement learning research for foundation models, including massive-scale self-play, end-to-end engineering from data curation to production, contribute to training/inference frameworks, publish and present results, engage with open-source and external collaborators.

Short-term intensive research internship assisting in dataset collection and preprocessing, model architecture design and tuning, implementation of testing frameworks, analysis of model accuracy and bias, algorithm refinement, and documentation to support foundation-model research.

Lead research to improve LLM efficiency and architectures, design and implement NLP algorithms (using PyTorch), integrate LLMs with vision models, optimize models and data pipelines, collaborate on code/design reviews, publish technical reports and papers, represent the institute at conferences, and contribute to ML infrastructure and model evaluation.

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.

Own end-to-end agentic data pipelines and evaluation suites: source and curate trajectories and tool-use data, design RL environments and rewards, build reliable large-scale eval harnesses, run experiments, diagnose regressions, and produce research outputs and technical reports.

Design, extend, and maintain distributed training infrastructure: modify frameworks (DeepSpeed, FSDP, etc.), implement distributed optimizers, build multi-node launch/config systems, implement experiment tracking and monitoring, optimize reliability and performance, and write production-quality ML infra code in PyTorch or JAX.

Lead product vision and strategy for foundation model families (LLMs, reasoning, world models). Define user personas and roadmaps, translate research into production requirements, prioritize features, own release criteria, drive cross-functional execution, and build/scale the Product Management organization while ensuring performance and safety for public releases.

Intern will build and validate a simulator and profiling framework to model inference on NVIDIA GPUs, develop analytical GPU kernel performance models, profile kernels with Nsight tools, analyze PTX/SASS, identify bottlenecks, and recommend kernel and system-level optimizations for transformer inference on Hopper and Blackwell architectures.

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.

Build and productionize distributed pre-training stacks (PyTorch/JAX) at multi-node GPU scale, implement new optimizers and high-performance CUDA/Triton kernels, optimize mixed-precision training and memory/communication, develop experiment infrastructure and monitoring, and mentor junior engineers to accelerate foundational model research.