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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Deep Learning Software Engineer, Inference and Model Optimization - **Company:** NVIDIA Corporation - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $184,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Program Optimization, Nvidia CUDA, Software Debugging, Python (Programming Language), Machine Learning, Software Architecture, Tensorflow, Software Engineering, Software Systems, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Information Technology, Optimization Algorithms, Deployment Automation, HuggingFace, Machine Learning Operations, TensorRT - **Published:** August 28, 2026 - **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Deep-Learning-Software-Engineer--Inference-and-Model-Optimization_JR1997596 ## About the Role * Masters, PhD, or equivalent experience in Computer Science, AI, Applied Math, or related field. * 5+ years of relevant work or research experience in Deep Learning. * Excellent software design skills, including debugging, performance analysis, and test design. * Strong proficiency in Python, PyTorch, and related ML tools (e.g. HuggingFace). * Strong algorithms and programming fundamentals. * Good written and verbal communication skills and the ability to work independently and collaboratively in a fast-paced environment. Ways to stand out from the crowd: * Contributions to PyTorch, JAX, or other Machine Learning Frameworks. * Knowledge of GPU architecture and compilation stack, and capability of understanding and debugging end-to-end performance. * Familiarity with NVIDIA's deep learning SDKs such as TensorRT. * Prior experience in writing high-performance GPU kernels for machine learning workloads in frameworks such as CUDA, CUTLASS, or Triton. ## Description We are now looking for a Senior Deep Learning Software Engineer to develop and scale up our automated inference and deployment solution. As part of the team, you will be instrumental in pushing the limits of inference efficiency and large-scale, automated deployment. Your work will touch upon fundamental aspects of a typical machine learning stack including working in high-level frameworks like PyTorch and HuggingFace to developing and improving high-performance kernel implementations in CUDA, TRT-LLM, and Triton. This is an exceptional opportunity for passionate software engineers straddling the boundaries of research and engineering, with a strong background in both machine learning fundamentals and software architecture & engineering., * Train, develop, and deploy state-of-the generative AI models like LLMs and diffusion models using NVIDIA's AI software stack. * Leverage and build upon the torch 2.0 ecosystem (TorchDynamo, torch.export, torch.compile, etc...) to analyze and extract standardized model graph representation from arbitrary torch models for our automated deployment solution. * Develop high-performance optimization techniques for inference, such as automated model sharding techniques (e.g. tensor parallelism, sequence parallelism), efficient attention kernels with kv-caching, and more. * Collaborate with teams across NVIDIA to use performant kernel implementations within our automated deployment solution. * Analyze and profile GPU kernel-level performance to identify hardware and software optimization opportunities. * Continuously innovate on the inference performance to ensure NVIDIA's inference software solutions (TRT, TRT-LLM, TRT Model Optimizer) can maintain and increase its leadership in the market. * Play a pivotal role in architecting and designing a modular and scalable software platform to provide an excellent user experience with broad model support and optimization techniques to increase adoption. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [30 Golden Rules of Deep Learning Performance](https://www.wearedevelopers.com/videos/11-30-golden-rules-of-deep-learning-performance) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Got AI ideas but no money? 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