AI Infrastructure and Frameworks Intern, Cosmos Lab - 2027
At a glance
Junior ML Platform & Ops role at NVIDIA. China, Beijing · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Develop and tune systems for pre-training, supervised fine-tuning and reinforcement learning on Physical AI world models
- Link simulation or real-robot interaction with experience gathering, rollout inference, reward computation, training and evaluation
- Make workflows more efficient by changing partitioning, pipelining, data movement and synchronization across execution modes
- Use profiling and benchmarks with researchers to find bottlenecks and measure throughput, latency, GPU use and policy freshness
- Present findings in tested code, documentation and technical talks, and help with research publications when appropriate
What you bring
- You are studying computer science, computer engineering, electrical engineering or a related subject at bachelor’s, master’s or PhD level
- You have strong Python and debugging ability, alongside systems knowledge in areas such as concurrency, distributed execution or memory management
- You have hands-on experience in training, RL, simulation or robotics integration, or inference infrastructure; coursework and projects qualify
Who this fits
This internship suits a student who wants to work on systems improvements using real AI workloads, with guidance from a mentor. Your project will be scoped to your experience and the length of the internship. Curiosity, analytical and communication skills, and eagerness to learn are valued.
From the employer
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.