AI Infrastructure and Frameworks Intern, Cosmos Lab - 2027

Overview

• Join NVIDIA’s Cosmos Lab Infrastructure team to develop training and post-training systems for advanced Physical AI models, including world foundation models and robot policies.
• Our infrastructure connects training, inference, and evaluation with simulation and real-world robot interaction.
• You will work with a mentor on a focused project scoped to your experience and internship duration, implementing and evaluating systems improvements on real AI workloads using NVIDIA’s GPU infrastructure.
• What you’ll be doing: Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL).
• Explore distributed parallelism, sharding, low-precision training, compute–communication overlap, and numerical consistency and efficient weight synchronization between training and inference.
• Build Physical AI post-training and RL infrastructure supporting advanced training algorithms.
• Connect simulation or, where applicable, real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation.
• Optimize these workflows through partitioning, pipelining, data transfer, and synchronization across synchronous, asynchronous, or disaggregated execution.
• Improve efficiency and scalability across training, inference, simulation, and evaluation through scheduling, placement, dynamic resource allocation, and load balancing, supporting heterogeneous resources, elasticity, and fault recovery.
• Analyze and optimize system performance, working with researchers to investigate, support, and compare emerging Physical AI models, training workflows, and algorithms from a systems perspective.
• Use profiling, benchmarking, and performance modeling to identify bottlenecks and measure throughput, latency, GPU utilization, and policy freshness.
• Share findings through tested code, documentation, and technical presentations, and contribute to research publications where appropriate.
• What we need to see: Pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
• Strong Python and debugging skills, with systems fundamentals in concurrency, distributed execution, memory management, or data movement.
• Practical experience in at least one area: training infrastructure, RL infrastructure, simulation or robotics integration, or inference infrastructure.
• Coursework, research, open-source projects, and internships all count.
• Strong analytical and communication skills, curiosity, and a willingness to learn.
• Experience in every listed area, prior access to large GPU clusters, and model architecture or learning algorithm research are not required.
• Ways to stand out from the crowd: Experience optimizing training infrastructure, including distributed parallelism, low-precision training, GPU memory efficiency, or compute–communication overlap.
• Experience optimizing scheduling, placement, resource allocation, or data transfer across training, rollout, simulation, and evaluation.
• Experience extending RL pipelines, integrating simulation environments or robot interfaces, or optimizing inference; GPU profiling, C++/CUDA development, and open-source contributions or research in ML systems are also valued.