AI Infrastructure and Frameworks Intern, Cosmos Lab - 2027

Responsibilities

• 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…
• 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…
• 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…
• 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.

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