Senior System Software Engineer - LocalAI
NVIDIA
Senior System Software Engineer - LocalAI at NVIDIA is a full time role based in Pune, India. It was published on 25 July 2026 and was open at last check.
| Role | Senior System Software Engineer - LocalAI |
|---|---|
| Company | NVIDIA |
| Location | Pune, India |
| Employment type | Full Time |
| Published | 25 July 2026 |
| Status | Open at last check |
NVIDIA has continuously reinvented itself for more than two decades. The invention of the GPU in 1999 fueled the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. GPU-powered deep learning helped ignite the era of modern AI, establishing GPUs as the foundation of intelligent applications across productivity, gaming, and creative workflows, and reinforcing NVIDIA’s position as a leading AI computing company.
More recently, there is a growing focus on running AI models locally, closer to where data is generated. This approach reduces latency, enables real-time processing, and addresses privacy concerns by minimizing the need to send data to centralized servers. As technology continues to evolve, client-side AI will play an increasingly important role in shaping the digital landscape. The LocalAI team is seeking a Senior Systems Software Engineer to develop efficient on-device AI software for RTX and DGX-class systems. The role focuses on delivering high-performance local inference with low latency, optimized memory utilization , robust infrastructure, and practical deployment on resource-constrained platforms.
What You’ll Be Doing:
Partner with NVIDIA’s software, research, architecture, and product teams to align technical requirements and strategic priorities, fostering the AI ecosystem on RTX and DGX PCs. Build and optimize the local AI inference stack for RTX, RTX Pro, and DGX GPUs, with a focus on performance, stability, and scalability across diverse hardware architectures. Design and develop modern inference runtimes and execution stacks using frameworks such as llama.cpp, vLLM , PyTorch , WinML , DXCGC, and TensorRT-RTX, supporting LLM, vision-language, TTS, ASR, and diffusion-based AI workloads. Perform end-to-end optimization of AI models, data pipelines, and inference runtimes to maximize performance on current and next-generation GPU architectures. Apply model optimization techniques, including quantization, pruning, sparsity, and distillation, to enable efficient deployment of large models on local and edge devices. Conduct system-level debugging, performance tuning, and performance-accuracy trade-off analysis; develop infrastructure for performance and accuracy sweeps; analyse results to identify gaps and drive fixes; and establish engineering guidelines to accelerate bring-up and ensure production readiness of new models and inference backends.
What we need to see:
5+ 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 foundation in data structures, algorithms, and machine learning. Proven experience developing and optimizing AI inference pipelines and applications using ML/DL frameworks such as Llama.cpp, vLLM , PyTorch , Windows ML , DXCGC, and TensorRT. Deep understanding of inference backends and runtime internals, including scheduling, memory management, KV-cache behaviour , graph execution, quantization, and hardware-aware optimization. Strong analytical and problem-solving skills, with the ability to manage multiple priorities effectively in a fast-paced environment. Excellent written and verbal communication skills, enabling effective collaboration across engineering teams and management.
Ways to stand out from the crowd:
Understanding of modern machine learning, deep neural network, and generative AI techniques, with relevant contributions to major open-source projects. Consistent track record of delivering end-to-end products in multinational companies with geographically distributed teams. Proficiency in low-level system and GPU programming, CUDA, and the development of high-performance systems. Contributions to open-source inference runtimes, model tooling, or performance infrastructure. Hands-on experience building applications using frameworks and APIs such as llama.cpp, PyTorch , TensorRT, Vulkan, DirectX, and vLLM .
We're a top employer recognized for innovation, growth, and a commitment to diversity as an equal-opportunity workplace. We offer competitive salaries, a generous benefits package, and the opportunity to work alongside some of the technology industry's most talented and forward-thinking professionals. As our engineering teams continue to grow rapidly, we're looking for creative, self-driven engineers with a passion for technology to join us.
Create your free OnJob profile to apply — we'll take you to NVIDIA's application after sign-up. · Posted 25 Jul 2026.
Senior System Software Engineer - LocalAI at NVIDIA — questions answered
What does the Senior System Software Engineer - LocalAI role at NVIDIA pay?
NVIDIA does not publish a salary on this Senior System Software Engineer - LocalAI listing, so OnJob shows no figure for it rather than an estimate. For what this role pays across the market, the OnJob salary guides aggregate the live listings that do disclose pay.
Where is the Senior System Software Engineer - LocalAI role at NVIDIA based?
NVIDIA lists this Senior System Software Engineer - LocalAI role in Pune, India, advertised as full time work at that location. Larger employers sometimes cover several sites under one city name, so confirm the exact office with NVIDIA before you apply.
Is the Senior System Software Engineer - LocalAI role at NVIDIA still open?
The Senior System Software Engineer - LocalAI posting at NVIDIA was open at OnJob's last check of the employer's careers page, having been published on 25 July 2026. OnJob re-checks source listings on each build and marks a role closed once it disappears, but listings can close without notice, so the employer's own page is the final word.
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