Enterprise Solutions Engineer jobs

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.

Collaborating with your peers across various engineering groups, you will optimally launch new systems for NVIDIA HGX GPU Accelerated Server Platforms to production. These purpose-built systems are optimized for the growing Deep Learning, Artificial Intelligence, and Analytics environments. With world-class technology enablingnever-been-seen-beforeperformance levels, NVIDIA’s HGX portfolio is arguably the most complicated systems platform ever developed by humans. This product family represents the company’s fastest growing line of business as well as its largest total available market opportunity. You will bring to bear your knowledge of system architectures and GPU technology in order to productize new GPU boards for datacenter architectures with GPU-accelerated clusters. Your responsibilities will include planning and establishing processes, defining test requirements and optimizing the production line to deliver new GPU boards. You will also be instrumental in helping the team to achieve the desired cost and quality metrics considered outstanding.

This position will be based out of Dallas, Texas.

What you will be doing:

• Use your in-depth experience with high speed networks and signals to plan and develop new diagnostic tests and debug procedures for next gen products

• Use your knowledge of system power-up and handshakes during boot to debug complex interactions between HW, FW and SW on faulty boards

• Recommend, drive and ensure compliance to DFx requirements for robust signal integrity performance as related to layout, mechanical components, assembly procedures, etc.

• Develop and deliver test specs for system level manufacturing screens for all new products to meet the required HW coverage, quality and product requirements.

• We collaborate with CM to define product assembly line, number of test stations and number of assembly fixtures, optimized for cost and efficiency.

• Craft creative solutions and WARs through volume data analysis and lab experimentation to solve meaningful yield and test problems.

• We lead optimization and continuous improvement efforts on the production screen spec definition processes to minimize waste and meet test time, yield, DPPM requirements.

• Support customer facing and quality teams during partner concerns to understand the issue and fix gaps identified in coverage.

What we need to see:

• BS or MS degree in EE/CE or equivalent experience.

• 5+ years of meaningful industry experience.

• Strong EE fundamentals, knowledgeable in digital design, signal integrity, statistics, timing analysis, fault analysis, sampling and computer architecture.

• Ways to stand out from the crowd:

• Prior board/system level electrical design experience.

• Experience with Perl, C/C++, Windows, and Linux.

What you will be doing:

• Work in a combined design and verification team which develops core units within the Networking silicon.

• Build reference models, verify and simulate chip blocks/entities according to specifications and performance requirements.

• Work closely with multiple teams within organizations such as Architecture, Micro- Architecture, FW and Post-Silicon validation.

• Lead the verification effort for core switching fabrics, packet buffering architectures, and traffic management subsystems, ensuring functional correctness and performance at scale.

What we need to see:

• B.SC./ M.SC. in Computer Engineering /ElectricalEngineering/CommunicationEngineering or equivalent experience

• 8+ years of proven experience in Design Verification.

• Proven, hands-on experience verifying sophisticated network switching ASICs, with deep knowledge of core switching logic and advanced buffering architectures

• Demonstrated ability to own verification strategy and execution from the block-level through full-chip (SoC) integration.

• Sharp analytical and debugging skills with a track record of root-causing complex hardware, testbench, or software-driven issues.

• Excellent organizational and communication skills, with the ability to manage priorities and negotiate solutions across design, verification, and architecture teams.

Ways to stand out from the crowd:

• Deep experience verifying advanced traffic management (TM) and Quality of Service (QoS) features, such as complex schedulers, shapers, and congestion control mechanisms

• Proven experience building performance-focused verification environments to measure and stress test latency, throughput, and fairness

• Knowledge in SimVision and Xcelium

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