AI Infrastructure and Frameworks Intern

NVIDIA

. Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL) .

Posted 9/17/2026internshipBeijing • ChinaEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and optimizing training infrastructure for Physical AI, with a focus on reinforcement learning, distributed parallelism, and efficient resource allocation. Proficient in Python and systems fundamentals, with strong analytical and communication skills to support research and technical documentation.

Highest-signal resume keywords

Python ProgrammingReinforcement Learning InfrastructureDistributed ParallelismGPU ProfilingSystems Fundamentals

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Training Infrastructure OptimizationLow-Precision TrainingMemory ManagementData MovementSchedulingResource AllocationData TransferPerformance ModelingBenchmarkingDebugging

Soft Skills

Analytical SkillsCommunication SkillsCuriosityWillingness to Learn

Tools & Technologies

GPU ClustersC++CUDASimulation EnvironmentsRobotics Integration

Industry Keywords

Physical AIModel ArchitectureTraining WorkflowsInference InfrastructureOpen-Source Contributions

Tech Stack

Tools & technologies

PythonC++

About the role

Key responsibilities & impact

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, 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 real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation
Optimize 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
Support heterogeneous resources, elasticity, and fault recovery
Analyze and optimize system performance with researchers
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
Contribute to research publications where appropriate

Requirements

What you’ll need

Pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field
Strong Python and debugging skills
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 as practical experience
Strong analytical and communication skills, curiosity, and willingness to learn
Experience in every listed area, prior access to large GPU clusters, and model architecture or learning algorithm research are not required
Experience optimizing training infrastructure, including distributed parallelism, low-precision training, GPU memory efficiency, or compute–communication overlap is valued
Experience optimizing scheduling, placement, resource allocation, or data transfer across training, rollout, simulation, and evaluation is valued
Experience extending RL pipelines, integrating simulation environments or robot interfaces, or optimizing inference is valued
GPU profiling, C++/CUDA development, and open-source contributions or research in ML systems are valued

Benefits

Comp & perks

Internship opportunity at NVIDIA’s Cosmos Lab Infrastructure team
Work with a mentor on a focused project scoped to experience and internship duration
Opportunity to work on real AI workloads using NVIDIA’s GPU infrastructure
Opportunity to contribute to research publications where appropriate