Senior Design Engineer 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:

• 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 Will Be Doing:

• Bring new products and new technologies to high volume manufacturing

• Deliver datamation solutions for enhanced decision making

• Manage supplier performance evaluation matrix and keep it updated based on changing business needs

• Drive continuous improvement in manufacturing yield, cycle time and product quality/reliability

• Lead change control through qualification and notification

• Lead non-conforming material disposition

• Manage BOM supply chain and optimize sourcing solutions for best capacity and cost

What We Need To See:

• Master’s degree or higher in Engineering or Science with emphasis on Mechanical, Materials Science, Chemical Engineering fields (or equivalent experience)

• 12+ years of validated experience in Flip Chip semiconductor packaging design, process and materials development. Direct experience in advanced assembly technologies such as 2.5D/3D packaging, Photonics packaging experience is desirable

• Solid technical knowledge of materials, process, package design and equipment technologies in substrate, bumping, and chip assembly

• Strong skills in project management and supplier management

• Solid understanding of quality control, statistics process control, gage R&R and DOE techniques

• Good knowledge of packaging industry and its supply chain

• Excellent written and verbal communication skills

• Experience working with multi-functional teams across the world

What you'll be doing:

• Path-find technical innovations in Quantum Error Correction and Fault Tolerance, working with multi-functional teams in Product, Engineering, and Applied Research

• Develop novel approaches to quantum error correction codes and their logical operations, including methods for implementation and logical operation synthesis

• Research and co-design improved methods to achieve fault tolerance, such as techniques for logical operations, concatenation, synthesis, distillation, cultivation, or others

• Collaborate with internal teams and external partners on developing technology components to enable a fault-tolerant software stack integrated with quantum hardware

• Adopt a culture of collaboration, rapid innovation, technical depth, and creative problem solving

What we need to see:

• Degree in Physics, Computer Science, Chemistry, Applied Mathematics, or related engineering field or equivalent experience (Ph.D. preferred)

• Extensive background in Quantum Information Science with 8+ overall years of experience in the Quantum Computing industry

• A demonstrated ability to deliver high impact value in quantum error correction and fault tolerance

Ways to stand out from the crowd:

• Hands-on experience in scientific computing, high-performance computing, applied machine learning, or deep learning

• Experience with co-design of quantum error correction with quantum hardware or quantum applications

• Experience with CUDA and NVIDIA GPUs

• Passion to drive technology innovations into NVIDIA software and hardware products to support Quantum Computing