Vice President Solutions Architecture jobs at Nvidia

What you'll be doing:

• Work with architects, designers, and performance engineers to develop an energy-efficient GPU.

• Identify key design features and workloads for building Machine Learning based unit power/energy models.

• Develop and own methodologies and workflows to train models using ML and/or statistical techniques.

• Improve the accuracy of trained models by using different model representations, objective functions, and learning algorithms.

• Develop methodologies to estimate data movement power/energy accurately.

• Correlate the predicted energy from models built at different stages of the design cycle, with the goal of bridging early estimates to silicon.

• Work with performance infrastructure teams to integrate power/energy models into their platforms to enable combined reporting of performance and power for various workloads.

• Develop tools to debug energy inefficiencies observed in various workloads run on silicon, RTL, and architectural simulators. Identify and suggest solutions to fix the energy inefficiencies.

• Prototype new architectural features, build an energy model for those new features, and analyze the system impact.

• Identify, suggest, and/or participate in studies for improving GPU perf/watt.

What we need to see:

• Pursuing or recently completed a MS or PhD in Electrical Engineering, Computer Engineering, Computer Scienceor equivalent experience.

• Strong coding skills, preferably in Python, C++.

• Background in machine learning, AI, and/or statistical modeling.

• Background in computer architecture and interest in energy-efficient GPU designs.

• Familiarity with Verilog and ASIC design principles is a plus.

• Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.

• Basic understanding of fundamental concepts of energy consumption, estimation, and low power design.

• Desire to bring quantitative decision-making and analytics to improve the energy efficiency of our products.

• Good verbal/written communication and interpersonal skills.

What you’ll be doing:

• Working with Cloud Service Providers to develop and demonstrate solutions based on NVIDIA’s groundbreaking software and hardware technologies.

• Build and deploy solutions at scale using NVIDIA's AI software on cloud-based GPU platforms.

• Build custom PoCs for solutions that address customer's critical business needs while applying NVIDIA's hardware and software technologies.

• Partner with Sales Account Managers or Developer Relations Managers to identify and secure business opportunities for NVIDIA products and solutions.

• Conduct regular technical customer meetings for project/product details, feature discussions, intro to new technologies, and debugging sessions.

• Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.

What we need to see:

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.

• 7+ years of Solutions Engineering (or similar Sales Engineering roles) experience.

• Established track record of deploying AI/ ML solutions in cloud environments including AWS, GCP, Azure or OCI

• Knowledge of DevOps/MLOps technologies such as Docker/containers, Kubernetes, data center deployments, etc.

• Effective time management and capable of balancing multiple tasks.

• Ability to communicate ideas clearly through documents, presentation, etc.

Ways to stand out from the crowd:

• AWS, GCP, Azure or OCI Professional Solution Architect Certifications

• Hands-on experience with NVIDIA GPUs and SDKs (i.e. CUDA, Triton, TensorRT-LLM, etc.)

• Deep understanding of the full software development lifecycle, including best practices for system design, architectural patterns, and comprehensive testing.

• Solid working knowledge of Python

• System-level experience, specifically GPU-based systems

What you'll be doing:

• Working with NVIDIA AI Native customers on data center GPU server and networking infrastructure deployments.

• Guiding customer discussions on network topologies, compute/storage, and supporting the bring-up ofserver/network/clusterdeployments.

• Identifying new project opportunities for NVIDIA products and technology solutions in data center and AI applications.

• Conducting regular technical meetings with customers as a trusted advisor, discussing product roadmaps, cluster debugging, and new technology introductions.

• Building custom demonstrations and proofs of concept to address critical business needs.

• Analyzing and debugging compute/network performance issues.

What we need to see:

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or related fields, or equivalent experience.

• 5+ years of experience in Solution Engineering or similar roles.

• System-level understanding of server architecture, NICs, Linux, system software, and kernel drivers.

• Practical knowledge of networking - switching & routing for Ethernet/Infiniband, and data center infrastructure (power/cooling).

• Familiarity with DevOps/MLOps technologies such as Docker/containers and Kubernetes.

• Effective time management and ability to balance multiple tasks.

• Excellent communication skills for articulating ideas and code clearly through documents and presentations.

Ways to stand out from the crowd:

• External customer-facing skills and experience.

• Experience with the bring-up and deployment of large clusters.

• Proficiency in systems engineering, coding, and debugging, including C/C++, Linux kernel, and drivers.

• Hands-on experience with NVIDIA systems/SDKs (e.g., CUDA), NVIDIA networking technologies (e.g., DPU or equivalent experience, RoCE, InfiniBand), and/or ARM CPU solutions.

• Familiarity with virtualization technology concepts.

As part of the NVIDIA Solutions Architecture team, you will navigate uncharted waters and gray space to drive successful market adoption by balancing strategic alignment, data-driven analysis, and tactical execution across engineering, product, and sales teams. You will serve as a critical liaison product strategy and large-scale customer deployment.

What you’ll be doing:

• Lead the end-to-end execution for key Hyperscalers customers to optimally and rapidly go-to-market at scale with NVIDIA data center products (e.g., GB200).

• Partner with Hyperscalers Product Customer Lead to understand strategy, define metrics, ensure alignment.

• Data-Driven Execution: Collect, maintain, and analyze sophisticated data trends to assess the product's market health, identify themes, challenges, and opportunities, and guide the customer to resolution of technical roadblocks.

• Problem Solving & Navigation: Navigate complex issues effectively, embodying a productive leader who balances short-term unblocks with long-term process and product improvements.

• Executive Communication: Deliver concise, direct executive-level updates and regular status communications to multi-functional leadership on priorities, progress, and vital actions.

• Process Improvement: Integrate insights from deployment challenges and customer feedback into future developments for processes and products through close partnership with Product and Engineering teams.

What we need to see:

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.

• 8+ years of combined experience in Solutions Architecture, Technical Program Management, Product Management, System Reliability Engineer or other complex multi-functional roles.

• Proven track record to lead and influence without direct authority across technical and business functions.

• Proven analytical skills with experience in establishing benchmarks, collecting/analyzing intricate data, and redefining data into strategic themes, action items, and executive summaries.

• Skilled in reviewing logs and deployment data, and aiding customers in resolving technical concerns (e.g., identifying performance issues associated with AI/ML and system architecture).

Ways to stand out from the crowd:

• Lead multi-functional teams and influence interested parties to address challenges in customer datacenter deployments, ensuring cluster health and performance at scale.

• Established track record of driving a product from the pilot phase to at-scale deployment in a data center environment.

• Hands-on experience with NVIDIA hardware (e.g., H100, GB200) and software libraries, with an understanding of performance tuning and error diagnostics.

• Knowledge of DevOps/MLOps technologies such as Docker/containers and Kubernetes, and their relationship to data center deployments.

• Confirmed capacity to align, adopt, and disseminate insights among various internal teams (e.g., collaborating with other program leads).

What you’ll be doing:

• Be a technical specialist on GPU and networking products directly supporting sales account managers, working closely with the team to secure design wins.

• Actively establish the technical relationship with our customer’s engineers, management, and architects at focus accounts.

• Identify customer architectures and key product requirements in the CSP/OEM AI market.

• Provide onsite support to solve problems in hardware and software, and deep learning inference.

• Lead the support of the product through the design-in phase all the way to the product end of life.

• Create technical solutions such as hardware & software demos and example system designs.

• Provide technical and sales training to the direct sales team and channel partners.

• Establish strong relationships and communication channels with internal teams.

What we need to see:

• BS or MS in Engineering, Electrical Engineering, Physics, or Computer Science (or equivalent experience).

• 8+ years of work-related experience in the high-tech electronics industry in a system design role or technical customer support role.

• Capable of working in a constantly evolving environment without losing focus.

• Ability to multitask effectively in a dynamic environment.

• Expert analytical and problem-solving skills.

• Strong time-management and organization skills for coordinating multiple initiatives, priorities, and implementations of new technology and products into very complex projects.

• Strong written and oral communication skills in English with the ability to effectively collaborate with management and engineering.

Ways to stand out from the crowd:

• ARM and NVIDIA GPU development

• Hands-on troubleshooting of Ethernet physical layer issues, including signal integrity and link training failures.

• Embedded Linux System, APIs and similar embedded OS knowledge.

• Experience working with OEMs in the industrial, military, and ruggedized computing space.

• CPU and PCIe Architecture knowledge

What you'll be doing:

• Help our customers integrate and develop accelerated Simulation and AI pipelines for Robotics applications.

• Act as technical authority to guide customers on solving sophisticated technical challenges by implementing end-to-end solutions using NVIDIA products, platform SDK's and libraries.

• Engage with clients to guide them developing solutions for industrial digital twins, to generate synthetic data for Ai training, integrate Generative Ai workflows within their software, and enhance workflows applying NVIDIA's Omniverse Kit SDK and Cosmos.

• Partner with Sales, NVIDIA Engineering, Product and other Solution Architect teams to drive NVIDIA full stack adoption. Develop a deep understanding of customer workflows and requirements, lead proof-of-concepts evaluations and provide internal feedback to drive continuous product improvements.

• Share domain expertise with the NVIDIA solutions architect community and stay updated on advancements in robotics, AI, simulation and computer graphics.

What we need to see:

• Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent experience.

• 12+ years of work experience in development for real time graphics applications, simulation, and/or AI.

• Strong programming skills in Python and a high-performance language such as C++ for efficient system development.

• Practical knowledge on one or more 3D workflows and tools (e.g., Unity, Unreal Engine, Blender) and Robotics Simulation frameworks (e.g., NVIDIA PhysX and Isaac SIM, Gazebo, MujoCo, Pybullet, Drake).

• Excellent interpersonal skills to convey technical concepts to diverse audiences.

• Self-starter with passion for growth, learning, and multi-functional teamwork.

Ways to stand out from the crowd:

• Familiarity with NVIDIA Omniverse, Oomniverse foundation applicaitons like USD Composer, Isaac Sim or Isaac Lab and with relevant data formats like Pixar’s OpenUSD and URDF

• Experience with control and actuator modeling in simulation frameworks like MuJoCo for sim2real experiments.

• Knowledge of industrial workflows (eg layout planning, motion planning and navigation, control, localization & collision avoidance, computer vision, point cloud processing) and past exposure to robotics software frameworks like ROS/ROS2 is a plus.

• Knowledge of deep learning principles as they apply to robotics and familiarity with NVIDIA’s AI technologies like TensorRT, TAO or Nemo platforms.

• Possess a Master’s or PhD in Computer Science, Robotics, Engineering, or related field. Lead tech teams and guide projects.

What you will be doing:

• Building an end-to-end agentic AI applications that solve real-world enterprise problems across various industries.

• Serve as the primary technical domain expert for pre- and post-sale for partners, embedding deeply with them to design and deploy Generative AI solutions at scale. Maintain strong relationships with leadership and technical teams to drive adoption, and successful utilization of NVIDIA GenAI platforms.

• Accelerate partner/customer time to value by providing repeatable reference architecture guidance, building hands-on prototypes, and advising on standard methodologies for scaling solutions to productions.

• Establish the scope, success metrics, and evaluation criteria for partner-led customer projects, ensuring alignment to standardized and reproducible GPU-accelerated workflows.

• Enable strategic partners to build their own Professional Services, platforms and products by integrating and accelerating using NVIDIA technologies for high-impact customer workloads. You will proactively find opportunities to drive deeper adoption and utilization of NVIDIA's Generative AI products.

• Codify knowledge and operationalize technical success practices to help partners scale impact across industries and workloads.

What we need to see:

• MS or PhD degree in Computer Science/Engineering, Machine Learning, Data Science, Electrical Engineering or a closely related field (or equivalent experience).

• 5+ years of meaningful work experience in deploying AI models at scale as a Software Engineer or Deep Learning engineer.

• Consistent track record of building enterprise-grade agentic AI systems using open-source models and solid foundation in deep learning, with a particular emphasis on LLM and VLM.

• Hands-on experience with LLM and agentic frameworks (NeMo Agent Toolkit, LangChain, Semantic Kernel, Crew.ai, AutoGen) and evaluation and observability platforms. Comfortable building prototypes or proofs of concept

• Strong coding development and proficiency in Python, C++ and Deep Learning frameworks (PyTorch, or TensorFlow).

• Excellent communication and presentation skills to effectively collaborate with both internal executives, partners and customers.

Ways to stand out from the crowd:

• Demonstrate expertise in building applications and systems using NeMo Framework, Nemotron, Dynamo, TensorRTLLM, NIMs, AI Blueprints. And actively contribute to the open-source community.

• Take end-to-end ownership of projects, proactively acquiring new skills or knowledge as needed to drive success.

• Excel in fast-paced environments, adeptly managing multiple workstreams and prioritizing for the highest customer impact.

• Understanding of different advanced agent architectures and emerging communication protocols (MCP, OpenAI Agentic SDK, or Google A2A).

• NVIDIA GPUs and system software stacks (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink and others.