
10,000+ employees
Founded 1993
🤖 Artificial Intelligence
🎮 Gaming
Artificial Intelligence • Gaming • Automotive
NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.
🕒 February 19
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10,000+ employees
Founded 1993
🤖 Artificial Intelligence
🎮 Gaming
Artificial Intelligence • Gaming • Automotive
NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.
• Implement language and multimodal model inference as part of NVIDIA Inference Microservices (NIMs). • Contribute new features, fix bugs and deliver production code to TRT-LLM, NVIDIA’s open-source inference serving library. • Profile and analyze bottlenecks across the full inference stack to push the boundaries of inference performance. • Benchmark state-of-the-art offerings in various DL models inference and perform competitive analysis for NVIDIA SW/HW stack. • Collaborate heavily with other SW/HW co-design teams to enable the creation of the next generation of AI-powered services.
• PhD in CS, EE or CSEE or equivalent experience. • 5+ years of experience. • Strong background in deep learning and neural networks, in particular inference. • Experience with performance profiling, analysis and optimization, especially for GPU-based applications. • Proficient in C++, PyTorch or equivalent frameworks. • Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture. • Proven experience with processor and system-level performance optimization. • Deep understanding of modern LLM architectures. • Strong fundamentals in algorithms. • GPU programming experience (CUDA or OpenCL) is a plus
• equity • benefits
Apply Now🕒 February 19
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