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Senior Performance Engineer - LLM Inference Frameworks Experience: Not Available to Not Available years Location: Israel, Yokneam Skills: Python, PyTorch, HuggingFace, TensorRTLLM, vLLM, SGLang, Nsight Systems, PyTorch Profiler, FP8, INT4, Speculative Decoding, Context Caching Company Overview NVIDIA is hiring exceptional software engineers to build and optimize the core inference infrastructure for large language models. Join the TensorRTLLM team - the group defining how generative AI performs at global scale on NVIDIA GPUs. Were looking for engineers who love squeezing every drop of throughput, memory efficiency, and scalability out of modern model runtimes. Your work will directly shape the frameworks behind stateoftheart LLM inference used across NVIDIA and the AI community. Join us to redefine what fast means for LLM inference - building the frameworks that power the next generation of generative AI at scale. What you'll be doing: Design, implement, and optimize highperformance inference pipelines for large language models running on GPUs Profile and tune model execution across the stack - from scheduler design to kernel fusions and everything in-between Design and experiment with memory management strategies for improved memory bandwidth optimization and cache efficiency Innovate and Implement cutting-edge techniques such as Speculative Decoding, Context Caching, and FP8/INT4 quantization to push the boundaries of tokens-per-second-per-watt Develop and maintain benchmarking and testing systems that quantify latency, utilization, and efficiency What we need to see: Bachelor's, Master's, or higher degree in Computer Engineering, Computer Science, Applied Mathematics, or related computing-focused degree (or equivalent experience) 5+ years of relevant software development experience. Excellent Python programming skills, software design, and software engineering skills Experience working with deep learning frameworks like PyTorch and HuggingFace Experience profiling and debugging performance at all levels - Python runtime, PyTorch internals, and GPU utilization metrics Awareness of the latest developments in LLM architectures and LLM inference techniques Proactive and able to work without supervision Excellent written and oral communication skills in English Ways to stand out from the crowd: Contributions to inference frameworks such as TensorRTLLM, vLLM, SGLang, or similar systems Demonstrated expertise in performance modeling, memory optimization, distributed model execution or GPU execution workflows Handson experience with NVIDIA profiling tools (Nsight Systems, PyTorch Profiler, custom benchmarking harnesses) Solid grasp of the tradeoffs shaping inference efficiency: compute vs. memory, scheduling vs. batching, latency vs. throughput Widely considered to be one of the technology worlds 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. #LI-Hybrid .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.