NVIDIA

Solutions Engineer

• Provide dedicated technical support for energy customers
• Drive solution development and hands-on enablement for domain-specific simulation and surrogate modeling use cases
• Work across multiple NVIDIA software stacks
• Encourage and grow China’s energy ecosystem
• Develop solutions that can scale to the global NVIDIA energy vertical
• Bridge modern AI and simulation research with actual energy industry implementations
• Help build a new energy vertical from the ground up and shape emerging opportunities

Energy Solution Architect

Full-stack Engineer

• Lead NVIDIA Cloud Partner Day 2 operational readiness efforts.
• Collaborate with NVIDIA Cloud Partners to establish systems, procedures, automation, and operational methods for managing NVIDIA accelerated infrastructure after deployment and activation.
• Develop continuous validation methods for GPU, CPU, storage, and network health across large-scale AI clusters.
• Establish telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads.
• Build automated workflows to detect, isolate, drain, repair, validate, and return unhealthy infrastructure to service.
• Implement scalable GPU fleet lifecycle strategies, including driver and firmware administration, Kubernetes node maintenance, OS patching, configuration management, upgrades, and configuration drift identification.
• Translate NVIDIA NCP requirements and reference architectures into production practices, validation criteria, runbooks, automation, and measurable operational standards.
• Develop health signals, SLOs, metrics, acceptance criteria, and ongoing validation mechanisms for infrastructure reliability and service readiness.
• Develop reusable tooling, automation, implementation guides, runbooks, operational playbooks, and reference implementations across multiple NCP environments.

Senior Engineer, NCX

• Lead the architectural vision for a massive global platform and spearhead operationalization of internal frontier-class AI inference systems
• Architect and transform the global enterprise compute platform running thousands of nodes and tens of thousands of VMs and containers via OpenShift and KubeVirt
• Define service tiers, SLAs, and automated cluster lifecycles
• Build automated remediation pipelines, hardware watchdogs, and telemetry for pre-release, rack-scale GPU systems
• Collect and review system data for capacity planning amid extreme hardware supply constraints
• Develop strategies including public cloud bursting, hardware dogfooding, and evaluation of alternative compute architectures such as ARM
• Drive adoption of standard platforms through self-service architectures, APIs, and Terraform/OpenTofu providers
• Evaluate application architectures and lead migrations of massive legacy workloads, including large-scale long-running VDI environments, into modern Kubernetes orchestration

Principal Software Engineer – Compute Infrastructure

Network Engineer

• Spearhead the conception, development, and execution of cutting-edge data center networks for NVIDIA customers under the Plan, Design, & Implement framework
• Advance comprehensive technology solutions for strategic customers in AI Factory and Enterprise data center domains
• Identify areas for improvement in business technology
• Provide insights and recommendations to marketing and engineering teams to enhance product strategy
• Provide Ethernet expertise to customers during the sales cycle
• Design, architect, and test Ethernet networking solutions
• Work on multifunctional teams supporting server infrastructure builds, accelerated computing workloads, and GPU-enabled AI applications
• Craft and evaluate DevOps automation scripts for network operations
• Craft network architectures and develop switch configurations
• Implement network implementation and validation tasks for data centers

Senior Data Center Ethernet Network Engineer

• Serve as the first line of technical expertise between NVIDIA, AI consulting partners, and end customers
• Enable GSI developers to adopt NVIDIA Agent Toolkit, Nemotron, and NIM for AI offerings, platforms, and client services
• Construct complex AI agent workflows to analyze, strategize, perform tasks, access APIs, and fix errors autonomously
• Create pilots, demos, and MVPs
• Build relationships with key technical teams to evangelize accelerated computing and Agentic AI
• Engage developers, researchers, data scientists, IT managers, and senior leaders to identify challenges and create solutions
• Work with Partner Business Managers to craft actionable partnership and alliance strategies
• Partner with Solutions Architects, engineering, product, and business teams to build full-stack AI solutions for industry and enterprise use cases
• Articulate the business value of state-of-the-art AI solutions to industry executives and GSI leaders
• Develop long-lasting relationships with technology partners and help integrate NVIDIA into their solutions

Senior Solutions Architect, Consulting Partners

Compliance

• Analyze global cybersecurity regulations and legal mandates, converting compliance standards into technical specifications and engineering rules
• Optimize, adapt, and scale Secure Product Development Lifecycle processes across software, firmware, and hardware pipelines
• Lead end-to-end security evaluations and certification initiatives
• Conduct technical reviews and work with lab evaluators and engineering teams
• Implement automation frameworks and agentic AI workflows for compliance tracking, evidence collection, and security verification
• Partner with Hardware, Software, Product Management, and Legal teams
• Align technical roadmaps with evolving international security standards and improve product security compliance readiness

Senior Product Security Compliance Architect

• Engage with researchers and ML engineers to architect and optimize end-to-end training workflows for robotics foundation models, including World Models, VLAs, and WAMs
• Build proof-of-concepts, reference architectures, and agentic workflows to accelerate experimentation, benchmarking, and model improvement of NVIDIA’s robotics open model platforms, including Cosmos and GR00T
• Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems
• Improve utilization, throughput, and memory efficiency
• Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets including video, sensor data, and trajectories
• Collaborate with NVIDIA product and engineering teams to provide feedback shaping future Physical AI platforms
• Work across research, engineering, and customer teams, influencing product direction and applied AI adoption

Senior Solutions Architect, Robotics Foundation Model Training

Artificial Intelligence

• Stand up and validate complete DSX-aligned software stacks on multi-node GPU systems
• Capture dependencies, configuration order, validation steps, and operational handoffs
• Turn working deployments into technical content including reference architectures, quick-starts, installation and upgrade guides, troubleshooting runbooks, code examples, blogs, whitepapers, and demo videos
• Build reusable examples and automation with APIs, Python or shell scripting, infrastructure-as-code, containers, Kubernetes, Slurm, Helm, GitOps, and CI/CD
• Build demos, labs, and training for operating an AI factory, including deployment, tenant setup, upgrades, monitoring, scheduling, fault isolation, remediation, capacity management, and security
• Demonstrate how data center hardware, infrastructure and cluster management software, orchestration, AI platforms, and workloads operate as one system
• Test pre-release software using representative training and inference workloads
• Identify interoperability and resiliency issues and provide feedback to Product and Engineering
• Support solution architects, field teams, cloud and OEM partners, ISVs, and system integrators through repeatable assets, train-the-trainer sessions, live demos, and direct support
• Collaborate with open-source and cloud-native communities on practical integration approaches, documentation, usability, and DSX software stack development
• Use recurring customer, partner, field, and developer problems to set content priorities and recommend product improvements
• Present work in customer briefings, partner workshops, industry events, webinars, and internal training

Senior Technical Marketing Engineer – DSX AI Infrastructure Software

Engineer

• Design SRAM circuits using state-of-the-art technology processes
• Optimize circuits for performance, area, and power
• Collaborate with mask designers to create high-quality, dense pitch-matched layouts
• Verify functionality, electrical integrity, and robustness
• Improve and develop flows and methodologies for design automation, data collection, and analysis to ensure working silicon
• Design specialized RAM implementations across NVIDIA's processing chips and collaborate with processor architecture teams

Senior SRAM Engineer

• Solve hard Day 2 operations problems at scale alongside partner engineers
• Find causes, prototype approaches, validate solutions under representative load, and leave operational practices partners can run
• Help partners prepare operating models for new NVIDIA platforms, capacity, services, and use cases
• Drive adoption in live environments without degrading service
• Improve reliability, performance, and economics using incident frequency, recovery time, utilization, and cost-per-token measures
• Identify and help close Day 2 maturity gaps across people, process, tooling, telemetry, security, and incident response
• Convert validated work into operating procedures, reference architectures, assessments, automation, and agentic workflows
• Spot cross-partner patterns and provide field evidence to account teams, support, product, and engineering
• Improve NVIDIA's factory planning function

Senior Solutions Architect, Cloud Partner Operations

Technical Program Manager

• Partner with Engineering, Infrastructure, Software teams and leadership to drive critical AI capacity enablement and management programs
• Develop and mature foundational DGX Cloud capabilities and processes, including cluster/capacity bring-up and maintenance
• Gather technical requirements and develop comprehensive roadmaps
• Drive breakthroughs and ensure adherence to NVIDIA’s Product Lifecycle (PLC) process
• Use Jira and other program management platforms to structure engineering deliverables
• Collaborate internally and externally to understand partner capabilities, connect them to NVIDIA reference architectures, and drive execution
• Drive adoption of third-party and in-house solutions for deployments, support, security, compliance, and observability
• Establish metrics and KPIs to quantitatively demonstrate program value and impact
• Identify, resolve, and mitigate risks and issues affecting scope, schedule, and quality
• Develop and execute communication strategies, including regular presentations to NVIDIA executive leadership
• Promote continuous improvement and process improvements within cloud infrastructure operations

Senior Technical Program Manager, Cloud Infrastructure

Platform Engineer

• Manage license scheduling policy throughout the estate, including hierarchical project shares, shared and non-shared distribution policies, and preemption on license rather than slot
• Model license demand against entitlement to support evidence-based procurement negotiations
• Tune allocation policy through tape-out crunch to ensure flawless execution during peak contention
• Build telemetry systems for license utilization, including feature-level checkout patterns, queue-time attribution, and waste reduction
• Partner with CAD and project teams to translate business priorities into share configurations that hold up under load

Compute Platform Engineer, License Scheduling

DevOps Engineer

• Design and own the configuration schema for LSF cell deployment
• Build the deployment pipeline from configuration merge to production across a federated estate
• Implement staged rollout and rollback
• Eliminate configuration drift across scheduler cells and build tooling to prevent recurrence
• Establish a regression suite for scheduled LSF upgrades
• Collaborate with the LSF internals engineer to encode scheduler expertise into templates and policy
• Own the config-as-code foundation for NVIDIA’s EDA compute farm end to end

Senior Infrastructure Automation Engineer, Compute Platform

• Own scheduler behavior across 15–25 federated LSF cells, including mbatchd and mbschd tuning, scheduling cycle analysis, and contention patterns near host-count ceilings
• Diagnose MultiCluster forwarding problems, including remote queue sizing, forwarding policy, and cross-cluster pending behavior
• Set technical design for cell topology and federation as the farm grows
• Decide what belongs in a cell versus a new cell
• Work with the IaC engineer to encode scheduler policy into a configuration schema compatible with MultiCluster at scale
• Partner with CAD and methodology teams on 500GB+ memory jobs, interactive-versus-batch contention, and tape-out crunch bursts

Senior Compute Platform Engineer, LSF

• Perform ASIC design verification of fabric and interconnect IP blocks and full chip integration, covering DMA, routing, arbitration, ordering, flow control, and protocol compliance
• Develop and implement UVM-based test plans for fabric and interconnect verification at IP and system levels
• Design and implement constrained-random and directed SystemVerilog testbenches for interconnect fabrics, NoC topologies, and multi-agent SoC integrations
• Collaborate with Architecture, SW/FW, Design, Modeling, Emulation, and Post-Silicon Validation teams
• Drive silicon and platform verification strategies and methodologies for fabric and interconnect subsystems
• Verify correctness, performance, and compliance of on-chip fabrics and interconnect subsystems from IP bring-up through full-chip integration