NVIDIASoftware Engineering Intern, DLFW Comms - 2027
NVIDIA is hiring a Software Engineering Intern, DLFW Comms - 2027 in Shanghai, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include vLLM, PyTorch and JAX.
- Level classifies it as an internship.
- Level lists 2,700+ other open roles at NVIDIA.
AI in this role
Integrate advanced multi-GPU communication libraries into deep learning frameworks like PyTorch and vLLM to scale AI workloads.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are looking for a motivated Deep Learning engineer to integrate advanced communication technologies into AI stacks like PyTorch, vLLM, SGLang, TRT-LLM, and veRL. You will be working with the team that developed communication libraries -- such as NCCL and NVSHMEM -- for scaling Deep Learning applications. Your customers will have diverse multi-GPU needs, ranging from training on scales up to 100K GPUs to inference at microsecond latency. Communication performance between GPUs directly affects AI applications. Your work in AI toolkits will simplify these challenges for the community. This is an excellent opportunity for someone with an AI background to push the state of the art in this field. Are you ready to contribute to innovative technologies and help realize NVIDIA's vision?
What you'll be doing:
Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production.
Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models.
Author custom communication or fused compute-communication kernels to showcase ultimate performance on NV platforms.
Conduct in-depth research to achieve SOL GPU performance.
Build fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
Collaborate with a very dynamic team across multiple time zones.
What we need to see:
You are pursuing a M.S. or Ph.D. in CE/CS/EE with a strong background in communication, kernel authoring, and/or AI training/inference.
Rapid prototyping and development with Python, C++, CUDA or related DSLs (Triton, cuTe).
Solid understanding of LLM models and parallelisms.
Adaptability and passion to learn new areas and tools.
Flexibility to work and communicate effectively.
Ways to stand out from the crowd:
Development experience with frameworks such as PyTorch, JAX, TRT-LLM, vLLM, SGLang, or veRL.
Experience with DL communication patterns such as Expert Parallelism (EP), TP, DP & PP.
Experience with CUDA kernel optimization and profiling.
Experience with large-scale training or production inference stack.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
How we rate this
Software Engineering Intern, DLFW Comms - 2027 at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where distributed training was part of your work. What did you do?
- Tell me about a project where kernel development was part of your work. What did you do?
- Tell me about a project where gpu optimization was part of your work. What did you do?
- Walk me through how you've used vLLM in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Deep learning, Distributed Training, Kernel Development, GPU Optimization, and vLLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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