System Software Engineer - Local AI
NVIDIA Corporation (company site, opens in a new tab)4+ yrsPuneOn-siteDirect career pageEst. 28–42 LPA
Posted on 9 Sept
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Job description
Structured overview of role & requirements
About This Role
- Build and optimize the local AI inference stack for RTX, RTX Pro, and DGX GPUs prioritizing performance, stability, and scalability.
- Develop modern inference runtimes and execution stacks for frameworks like Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT-RTX across diverse AI workloads.
- Perform end-to-end optimization of AI models, data pipelines, and inference runtimes; apply model optimization techniques to enable efficient local and edge deployment.
Minimum Requirements
- 4+ years experience with a Bachelor's, Master's, or PhD in Computer Science, Software Engineering, Mathematics, or related field (or equivalent experience).
- Excellent C++ programming and debugging skills with strong understanding of data structures, algorithms, and machine learning.
- Proven experience with AI inference pipelines and ML/DL frameworks such as Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT.
- Deep knowledge of inference backends and runtime internals including scheduling, memory management, KV-cache behavior, graph execution, quantization, and hardware-aware optimization.
Ideal Candidate Profile
- Has strong integration experience working cross-functionally with software, research, architecture, and product teams in a complex AI hardware/software environment.
- Experienced delivering end-to-end local AI inference products with strong domain knowledge of GPU systems and ML frameworks.
- Demonstrates proficiency in system-level debugging and performance analysis, with a focus on production readiness and scalability of AI inference on resource-constrained platforms.