Principal Architect Digital Native jobs

What You’ll Be Doing:

Lead the product strategy and execution for NVIDIA’s Omniverse Digital Twin platform focused on Digital Twin robotics and automation integration use cases. Drive cross-functional alignment and deliver features that enable scalable, Physical AI-powered simulation solutions.

• Define and manage roadmap, use cases, and user journeys for industrial digital twins

• Translate customer needs into clear, prioritized requirements

• Partner with engineering to drive execution and technical trade-offs

• Serve as a voice of the customer across development and GTM teams

• Mentor and guide junior PMs to grow product leadership capacity

• POR & One-page summaries of strategic intent

• Product roadmap with quarterly milestones and long-term vision

• PRDs with use cases, personas, and workflows

• Capability and release launch plans in coordination with GTM and engineering

What We Need To See:

• 7+ years in product management, including experience owning sophisticated platform or developer-facing products.

• BS or MS in Computer Science, Engineering, or equivalent experience.

• Confident navigating cross-functional leadership and high-stakes decision-making, with the ability to lead product initiatives and drive alignment.

• Background in robotics, automation, simulation, or digital twins — or a proven track record to ramp up quickly in highly technical domains.

• A strategic and systems thinker, equally comfortable defining big-picture vision and execution details.

• Experience in both startup and large enterprise settings; adaptable, pragmatic, and process-aware.

Ways To Stand Out From The Crowd:

• Strong sense of purpose and ownership, with an ability to prioritize, deliver product, and encourage those around you.

• Humble, collaborative, and curious, always seeking new ways to tackle problems and empower your team.

What you’ll be doing:

• Understand, analyze, profile, and optimize AI training workloads on state-of-the-art hardware and software platforms.

• Guide development of future generations of artificial intelligence accelerators and systems.

• Develop detailed performance models and simulator infrastructure for computing systems accelerating AI training, and implement and evaluate hardware feature proposals.

• Collaborate across the company to guide the direction of machine learning at NVIDIA; spanning teams from hardware to software and research to production.

• Drive HW/SW co-design of NVIDIA’s full deep learning platform stack, from silicon to DL frameworks.

What we need to see:

• PhD in CS, EE or CSEE and 3+ years; or MS (or equivalent experience) and 6+ years of relevant work experience.

• Strong background in computer architecture, with a proven track record of architecting features in shipping high-performance processors.

• Background in artificial intelligence and large language models, in particular training algorithms and workloads.

• Experience analyzing and tuning application performance on state-of-the-art hardware.

• Experience with processor and system-level performance modelling, simulation, and evaluation before silicon exists.

• Programming skills in C++ and Python.

• Familiarity with GPU computing across all layers of the AI stack, from DL frameworks like PyTorch down to CUDA.

What you'll be doing:

• You will be contributing to power estimation models and tools for GPU products and systems like NVIDIA DGX.

• Early GPU & System Architecture exploration with focus on energy efficiency and TCO improvements at GPU and Datacenter level.

• You will help with Performance vs Power Analysis for NVIDIA future product lineup.

• Deploy machine learning techniques to develop highly accurate power and performance models of our GPUs, CPUs, Switches, and platforms.

• Understand the workload characteristics for GenAI/HPC workloads at Datacenter Scale (multi-GPU) to drive new HW/SW features for Perf@Watt improvements.

• Modeling & analysis of cutting-edge technologies like high speed & high-density interconnects.

What we need to see:

• Pursuing a MSEE/MSCE, or equivalent experience related to Power / Performance estimation and optimization techniques.

• Knowledge of energy efficient chip design fundamentals and related tradeoffs.

• Familiarity with low power design techniques such as multi-VT, Clock gating, Power gating, and Dynamic Voltage-Frequency Scaling (DVFS).

• Understanding of processors (GPU is a plus), system-SW architectures, and their performance/power modeling techniques.

• Proficiency with Python and data analysis packages like: Pandas, NumPy, PyTorch.

• Familiarity with performance monitors/simulators used in modern processor architectures.

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