AI Infrastructure and Frameworks Intern, Cosmos Lab - 2027
Salary not listed • China, Beijing • Internship
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Internship
Categories
AI Infrastructure Engineer
About the job
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.
Responsibilities
- Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL), exploring distributed parallelism, sharding, low-precision training, compute–communication overlap, and numerical consistency and efficient weight synchronization.
- Build Physical AI post-training and RL infrastructure supporting advanced training algorithms, connecting simulation or real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation, optimizing workflows through partitioning, pipelining, data transfer, and synchronization.
- 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, using 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.
Requirements
- 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.
- Strong analytical and communication skills, curiosity, and a willingness to learn.