9 days ago
Remote, Germany +2 moreSenior
Responsibilities
- Optimize and tune deep learning models for LLM, multimodal, and generative AI workloads.
- Scale model performance across different NVIDIA accelerator architectures, from datacenter GPUs to edge SoCs.
- Contribute features and code to vLLM, SGLang, FlashInfer, NVIDIA inference libraries, and related software solutions.
- Implement and optimize model-serving pipelines using CUDA kernels and GPU software tools.
- Collaborate with teams across frameworks, NVIDIA libraries, and inference optimization initiatives.
Requirements
- Master's degree, PhD, or equivalent experience in computer engineering, computer science, EECS, AI, or a relevant field.
- At least 5 years of relevant software development experience.
- Excellent C/C++ programming and software design skills.
- Python experience and software Agile skills are helpful.
- Experience training, deploying, or optimizing deep learning model inference in production is a plus.
- Background in performance modeling, profiling, debugging, code optimization, or CPU/GPU architecture is a plus.
- Experience with deep learning software projects such as PyTorch, vLLM, or SGLang; NCCL or NVSHMEM; enterprise product development; or CUDA, OAI Triton, or CUTLASS is advantageous.
Benefits
- Highly competitive salary and an extensive benefits package.
- Flexible and diversity- and inclusion-focused work environment.
- Base salary varies by location, experience, and other objective job-related factors, with listed ranges for roles in the Netherlands, Germany, and Poland.
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.