Senior Deep Learning Performance Architect - LPU
At a glance
Senior AI Research role at NVIDIA. Remote, United States · full-time.
In listing AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Create GPU and system designs that improve AI inference speed and efficiency
- Test widely used deep learning methods and applications, then investigate their behavior
- Study how hardware and software choices shape the algorithms and applications built for future systems
- Model power use and performance across AI inference, retaining key findings to inform later hardware designs
- Coordinate with software, research and product groups to help set AI direction
What you bring
- A relevant master's or doctoral degree, or equivalent experience, plus at least five years in the field
- Strong grounding in machine learning and deep learning mathematics
- Advanced programming ability in C, C++ or Python
- Working knowledge of GPU computing and high-performance computing frameworks
- Solid computer architecture knowledge, supported by relevant coursework
Who this fits
This senior role suits someone who models large language model performance and designs AI systems, with a focus on inference efficiency. It is a remote, full-time position based in the United States.
From the employer
We are now looking for a Senior Deep Learning Performance Architect!
NVIDIA seeks a Senior DL Performance Architect to join our group of pioneers who enjoy pushing AI Inference performance boundaries. Our team focuses on ambitious hardware-software co-design to speed AI Inference workloads. This role gives an outstanding opportunity to develop world-class performance strategies, guide future GPU architecture decisions, and lead AI innovation. If you are passionate about AI efficiency Pareto curves, have a proven record of modeling LLM performance and architecting AI systems, and enjoy optimizing every cycle, this role may be perfect for you!
What you'll be doing:
Design novel GPU and system architectures to advance the forefront of AI Inference performance and efficiency
Construct, investigate, and test popular deep learning algorithms and applications
Understand and analyze the relationship between hardware and software architectures as it influences future algorithms and applications
Build efficient power and performance models of AI inference stack, while capturing minimal but significant information to guide next-gen HW architecture
Collaborate across the company to guide the direction of AI, working with software, research, and product teams
What we need to see:
A MS or PhD in a relevant field (CS, EE, Math) or equivalent experience, with 5+ years of relevant experience
Strong mathematical foundation in machine learning and deep learning
Expert programming skills in C, C++, and/or Python
Familiarity with GPU computing (CUDA or similar) and HPC (MPI, OpenMP) stack
Strong knowledge and coursework in computer architecture
Ways to stand out from the crowd:
Background with systems-level performance modeling, profiling, and analysis
Experience in characterizing and modeling system-level performance, accomplishing comparison studies, and documenting and publishing results
Background in improving AI Inference workloads by developing CUDA kernels or compilers for custom ASIC hardware
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 15, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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.