# Deep Learning Model and System Optimization Engineering Intern – 2027 at NVIDIA

NVIDIA is hiring a Deep Learning Model and System Optimization Engineering Intern – 2027 in Shanghai, China. Level rates it Builds AI ●●●●; you can [apply on NVIDIA](https://jobsbylevel.com/go/6403ce19-dc1b-4b7d-b4eb-b219a8984cfd?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. China, Shanghai.

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 4 (score 90 out of 100)
- Location: China, Shanghai
- Posted: October 23, 2026
- Apply: https://jobsbylevel.com/go/6403ce19-dc1b-4b7d-b4eb-b219a8984cfd?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=deep-learning-model-and-system-optimization-engineering-intern-2027-at-nvidia-eadb6f

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## Description

We are now looking for a Deep Learning Performance Engineer Intern to join our team. Large language models are transforming how people interact with computing, while placing unprecedented demands on inference performance, memory efficiency, scalability, and deployment cost. Our team develops GPU-accelerated software for deep learning inference. We work across model architectures, algorithms, numerical representations, inference systems, and GPU architectures to continuously improve the performance and deployment capabilities of large language models on NVIDIA GPUs. Our work contributes to inference frameworks such as TensorRT-LLM and helps bring state-of-the-art models to production efficiently. We are looking for outstanding interns who are passionate about large language model inference, low-precision computing, GPU performance optimization, and hardware-software co-design. What you’ll be doing: Analyze and optimize inference performance across model architecture, numerical precision, GPU kernels, memory systems, and end-to-end serving frameworks. Develop low-bit quantization techniques for model weights, activations, attention operands, and KV caches. Explore numerical formats and mixed-precision strategies involving FP8, NVFP4, MXFP4, and other emerging low-precision formats while preserving model accuracy and capabilities. Design and evaluate techniques for KV cache compression, sparsification, hierarchical storage, and reuse across inference requests. Optimize the inference of model architectures including MHA, GQA, MQA, MLA, sliding-window attention, sparse attention, linear attention, and state space models. Investigate model-system co-optimization techniques such as speculative decoding, state reuse, and computation reuse. Build performance models and optimization strategies for long-context inference and agentic workloads, with a focus on throughput, time to first token, inter-token latency, memory efficiency, and deployment cost. Collaborate closely with model, framework, kernel, library, system software, and GPU architecture teams to influence the design of future models, software platforms, and hardware architectures. What we need to see: Pursuing an MS or PhD from a leading university in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related field, with an expected graduation date in 2028. Available to work as an intern for at least three months and at least four days per week. Strong understanding of Transformer architectures and large language model inference, including core components such as attention, MoE, and KV cache management. Experience with at least one large language model inference framework, such as TensorRT-LLM, vLLM, SGLang, or a comparable framework. Proficiency in Python and PyTorch, with strong experimental design, data analysis, debugging, and problem-solving skills. Experience with at least one GPU programming technology, such as CUDA C/C++, Triton, or a comparable parallel programming framework. Understanding of GPU architecture, memory hierarchy, Tensor Cores, parallel computing, and performance analysis. Familiarity with low-precision computing concepts, including quantization error, scaling, rounding, clipping, group or block quantization, and mixed-precision computation. Ability to evaluate an optimization technique across model accuracy, kernel performance, and end-to-end inference performance. Good communication, technical writing, organization, and cross-functional collaboration skills. Ways to stand out from the crowd: Hands-on experience optimizing kernels or end-to-end workloads on NVIDIA GPUs. Research or engineering experience in model quantization, KV cache optimization, efficient attention, speculative decoding, or long-context inference. Contributions to inference frameworks, GPU libraries, compilers, or other relevant open-source projects. Experience profiling and optimizing production-scale LLM inference workloads. NVIDIA is

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