We are looking for a Solutions Architect with a performance engineering background who can help our most sophisticated Autonomous Vehicles and Robotics customers accelerate Physical AI workloads using NVIDIA's full-stack technologies! As part of the Automotive Solutions Architecture team, we work with some of the most innovative accelerated computing platforms focused on the development and test of Autonomous Vehicles. We dive deep into customer projects to solve performance bottlenecks. We use insights from workloads to guide next-generation NVIDIA hardware and software. If you are driven by innovation and ambition, this is the team for you!
What you'll be doing:
Engaging directly with key AV application engineers to understand the current and future problems they are solving. You will develop and improve fundamental parallel algorithms and data structures. You will provide efficient solutions using GPUs through library development and direct application contributions.
Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the build of next-generation architectures, software platforms, and programming models.
Engaging in deep optimization of high-performance operators, involving but not limited to GPU kernel optimization, instruction-level tuning, and compiler optimization. These optimizations will directly support customers to use NVIDIA libraries such as cuDNN, cuBLAS, and CUTLASS and Open- source libs like DeepGEMM, FlashMLA, FlashAttention, Flashinfer, etc.
Improving communication for Physical AI-related distributed transformer workloads by employing communication tools developed by NVIDIA. These include NCCL, NCCL GIN, and NVSHMEM, as well as open-source solutions like DeepEP and NCCL EP. This demands in-depth study of interconnect topologies (NVLINK) and network protocols (InfiniBand/RoCE) to develop efficient data transfer strategies alongside techniques enabling compute-communication overlap.
What we need to see:
BSc/MSc/PhD or equivalent experience in Computer Science, Electrical Engineering, Physics, Mathematics, or a related technical field.
8+ years of hands-on validated ML/DL performance engineering experience with focus on improving GPU compute efficiency of training and inferencing workloads.
Experience with C, C++, or Python and proficiency with Linux.
Solid understanding of software development, programming techniques, and algorithms.
Strong mathematical fundamentals, including linear algebra and numerical methods.
Background in parallel programming and high-performance computing, with extensive knowledge of parallel architectures and methods for performance analysis and tuning. Experience in GPU programming is desirable.
Experience in distributed communication optimization is highly helpful. This involves familiarity with remote direct memory access, GPU interconnects, collective communication algorithms, and associated open-source libraries used in large-scale model training and inference.
Effective verbal/written communication, and technical presentation skills. Ability to communicate your ideas/code clearly through blog posts, GitHub, ppt.
Ways to stand out from the crowd:
Prior experience in writing CUDA kernels, and experience with Nsight System and Nsight Compute.
Experience in comprehensive evaluation and improvement of full-stack systems within at least one of these areas: LLM and HPC. Having expertise ranging from operator-level through framework-level to algorithm-level optimization is strongly preferred.
Proven software engineering fundamentals and system architecture thinking, with the ability to build modules and lead engineering approaches in complex systems.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
#NALASAHiring
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
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.Skills Required
- BSc/MSc/PhD or equivalent in Computer Science, Electrical Engineering, Physics, Mathematics, or related technical field.
- 8+ years of hands-on ML/DL performance engineering experience focused on GPU compute efficiency for training and inference.
- Experience with C, C++, or Python and proficiency with Linux.
- Solid understanding of software development, programming techniques, and algorithms.
- Strong mathematical fundamentals, including linear algebra and numerical methods.
- Background in parallel programming and high-performance computing with extensive knowledge of parallel architectures and performance analysis/tuning.
- Experience in GPU programming.
- Experience in distributed communication optimization (RDMA, GPU interconnects, collective algorithms, related open-source libraries).
- Effective verbal and written communication and technical presentation skills.
- Prior experience writing CUDA kernels and using Nsight System/Nsight Compute.
- Experience evaluating and improving full-stack systems in areas like LLM or HPC across operator, framework, and algorithm levels.
- Proven software engineering fundamentals and system architecture thinking.
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.”
