NVIDIA has been redefining computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, we lead in artificial intelligence, driving advances in natural language processing, computer vision, autonomous systems, and scientific research. We are looking for a forward-thinking HPC and AI Inference Software Architect to help shape the future of scalable AI infrastructure—focusing on distributed training, real-time inference, and communication optimization across large-scale systems.
Join our world-class team of researchers and engineers building next-generation software and hardware systems that power the most demanding AI workloads on the planet.
What you will be doing:
Design and prototype scalable software systems that optimize distributed AI training and inference—focusing on throughput, latency, and memory efficiency.
Develop and evaluate enhancements to communication libraries such as NCCL, UCX, and UCC, tailored to the unique demands of deep learning workloads.
Collaborate with AI framework teams (e.g., TensorFlow, PyTorch, JAX) to improve integration, performance, and reliability of communication backends.
Co-design hardware features (e.g., in GPUs, DPUs, or interconnects) that accelerate data movement and enable new capabilities for inference and model serving.
Contribute to the evolution of runtime systems, communication libraries, and AI-specific protocol layers.
Collaborate with customers to understand their needs and provide innovative solutions for them.
What we need to see:
Ph.D, Masters, or Bachelors in computer science, computer engineering, electrical engineering or a closely related field.
5+ years of experience in DNNs, Scaling of DNNs, Parallelism of DNN frameworks, or deep learning training workloads.
Deep understanding of Inference and Training workloads and optimizations, like Prefill/Decode, data parallelism, Tensor parallelism, FDSP, etc...
Experience with AI network parallelism using collective libraries and RDMA/RoCE.
Background in algorithm design, system programming, and computer architecture.
Strong programming and software development skills.
Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.
Ways to stand out from the crowd:
Deep understanding of technology and passion for what you do.
Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic matrix environment.
Background with designing communication middleware for high-performance computing systems, including RoCE and DPUs.
Background with CUDA programming and NVIDIA GPUs and programming models for emerging architectures.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
#LI-Hybrid
Skills Required
- Bachelor’s, Master’s, or Ph.D. degree in computer science, computer engineering, electrical engineering, or a closely related field
- 5+ years of experience in DNNs, DNN scaling, parallelism of DNN frameworks, or deep learning training workloads
- Deep understanding of inference and training workloads and optimizations, including Prefill/Decode, data parallelism, tensor parallelism, and FDSP
- Experience with AI network parallelism using collective libraries and RDMA/RoCE
- Background in algorithm design, system programming, and computer architecture
- Strong programming and software development skills
- Ability to work and communicate effectively in a multinational, multi-time-zone corporate environment
- Deep understanding of technology and passion for the field
- Strong collaborative and interpersonal skills, including the ability to guide and influence within a dynamic matrix environment
- Background designing communication middleware for high-performance computing systems, including RoCE and DPUs
- Background with CUDA programming, NVIDIA GPUs, and programming models for emerging architectures
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
