Nvidia
Machine Learning Engineering
Overview
The LocalAI team is seeking a Systems Software Engineer to build efficient on-device AI software for RTX and DGX-class systems. This role focuses on high-performance local inference, low latency, efficient memory use, infrastructure and practical deployment on resource-constrained platforms.
About Nvidia
NVIDIA has continuously reinvented itself for over two decades. The invention of the GPU in 1999 fueled the growth of PC gaming, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning has ignited modern AI, positioning NVIDIA as a leading AI computing company.
Requirements & Eligibility
- 2+ years of experience with Bachelor's, Master's, or PhD in Computer Science, Software Engineering, Mathematics, or a related field, or equivalent experience
- Excellent C++ programming and debugging skills, with a strong understanding of data structures, algorithms and machine learning
- Proven experience working with AI inferencing pipelines and applications using ML/DL frameworks, such as Llama.cpp, vLLM, PyTorch, WinML, DXCGC and TensorRT
- Deep interest in inference backends and runtime internals, including scheduling, memory management, KV-cache behavior, graph execution, quantization, and hardware-aware optimization
- Strong analytical and problem-solving abilities
- Outstanding written and oral communication skills
Key Responsibilities
- Partnering with NVIDIA's software, research, architecture, and product teams to align strategies and technical needs, encouraging the ecosystem of AI on RTX and DGX PCs
- Building and optimizing local AI inference stack for RTX, RTX Pro and DGX GPUs, focusing on performance, stability, and scalability across various hardware architectures
- Architecture and development of modern inference runtimes and execution stacks, covering frameworks like Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT-RTX across LLMs, vision-language, TTS, ASR, and diffusion AI workloads
- Perform end-to-end optimization of AI models, data pipelines, and inference runtimes to enhance performance across current and next-generation GPU architectures
- Apply model optimization techniques such as quantization, pruning, sparsity, and distillation to enable efficient deployment of large models on local and edge devices
- Perform system-level debugging, performance optimization, and performance–accuracy trade-off analysis
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