NVIDIA

Administration

• Serve as a specialist in NVIDIA's global Accounts Payable team in Santa Clara, CA
• Lead the strategy, administration, expansion, and control of the global virtual card payment program across the Americas, EMEA, and APAC
• Manage card payment cycles, card issuance, spending limits, and payment file exchanges between SAP and card-issuing bank systems
• Define and lead the global supplier enablement strategy, identifying card-eligible suppliers and running targeted onboarding campaigns
• Resolve complex supplier and bank inquiries involving declined, expired, or unprocessed cards, remittance difficulties, and payment rejections
• Perform daily and month-end reconciliation across entities and currencies, investigating open items, FX differences, refunds, and credits
• Build and monitor controls to detect unusual transactions, duplicate payments, and potential fraud; lead dispute and recovery efforts
• Track regional and entity-level program benchmarks, present insights to Finance leadership, and support accounting, rebate calculations, accrual, and forecasting
• Lead cross-functional initiatives involving IT, Finance Systems, Treasury, Tax, Procurement, and regional AP teams
• Manage requirements gathering, user acceptance testing, and cutover for payment configurations, system improvements, and international rollouts
• Support SOX adherence and audit readiness, including access reviews, segregation of duties, payment approvals, and control evidence
• Mentor AP team members, establish global procedures and supplier-facing materials, and lead automation and process-improvement initiatives

Global Virtual Card Payment Administrator

Application Engineer

• Serve as a technical specialist on GPU and networking products, supporting manufacturing, quality, and failure analysis teams to improve product reliability and yield
• Establish and nurture technical relationships with internal engineering teams, manufacturing partners, and quality teams
• Identify system failure modes, manufacturing risks, and product requirements to drive corrective actions and design/process improvements
• Maintain frequent on-site presence in labs or manufacturing sites to diagnose, reproduce, and resolve hardware and system-level failures
• Support the product lifecycle from NPI through high-volume manufacturing and field returns
• Develop failure analysis methodologies, test plans, and diagnostic tools for hardware validation
• Provide technical training and documentation to manufacturing, test, and quality teams
• Collaborate with design, validation, manufacturing, and quality teams to drive issue resolution, root cause determination, and corrective actions

Field Application Engineer

Product Marketing

• Define product launch strategy and drive go-to-market plans, including messaging, positioning, and product differentiation
• Align sales, marketing, partners, and customers to the product mission and revenue opportunities
• Develop product marketing strategies for product adoption and growth
• Create marketing and sales/partner enablement assets, including blogs, whitepapers, webinars, demos, and customer and partner decks
• Present ideas using slides, whiteboards, and other creative approaches
• Translate technology capabilities into messaging for various audiences
• Implement product marketing plans with product management, sales, campaign marketing, PR, and engineering teams

Senior Product Marketing Manager – Industrial and Computational Engineering

Artificial Intelligence

• Deploy, manage, and validate AI Compute/HPC infrastructure in Linux-based environments for new and existing customers
• Act as the domain expert with customers during planning calls through implementation
• Perform handover-related documentation and knowledge transfers to support customers rolling out sophisticated systems
• Provide feedback to internal teams by opening bugs, documenting workarounds, and suggesting improvements
• Interact with customers, partners, and internal teams to analyze, define, and implement large-scale AI Compute projects involving networking, system design, automation, and validation

Senior AI Compute Engineer

Attorney

• Design, implement, and document software tools, custom scripts, and automated workflows to streamline legal department processes
• Integrate and expand the core legal tech stack, including CounselLink, Anaqua, and DocuSign CLM, with enterprise systems, APIs, custom tools, and automated data pipelines
• Architect end-to-end technical solutions for legal intake triage, contract automation, IP operations, and spend analytics
• Work directly with the Legal Operations manager, attorneys, paralegals, and partners from IT and Finance to translate legal workflows into scalable software solutions
• Maintain user documentation, system architectures, and infrastructure to ensure stability and security across internal tools

Legal Operations Engineer

• Build the segment strategy, target-customer plan, pipeline, forecast, and competitive view for NVIDIA AI factory fabric software
• Identify and convert lighthouse customers into repeatable software and services growth
• Develop account and partner plans for fabric management and automation software and adjacent AI infrastructure offerings
• Shape deal strategy and resolve commercial blockers across pricing, packaging, entitlement, support, licensing, and partner routes
• Create scalable seller, OEM, and channel plays that translate technical capabilities into customer outcomes and measurable business value
• Establish operating metrics for pipeline quality, product adoption, active usage, paid attach, renewals, and expansion
• Use business signals to prioritize the segment
• Bring recurring customer and field feedback to Product, Product Marketing, Engineering, and Support
• Close the loop with the field on decisions and next steps
• Represent the business with technical and executive stakeholders and communicate recommendations clearly

Principal Segment Sales Manager – AI Factory Fabric Software

Engineer

• Lead design and development of NVIDIA’s Assembler and Disassembler for GPU compute
• Develop binary analysis and instrumentation features, including call graph generation, program register usage, and GPU binary patching
• Work with GPU architecture and debugger/profiler development teams to understand requirements and deliver features and product improvements
• Collaborate with teams developing related components to ensure compatibility, reliability, and high-quality code generation
• Work with customers and partners to collect feedback and drive innovative product ideas and features
• Develop tools enabling high-performance applications and libraries for HPC, deep learning, and autonomous driving
• Contribute to assembler and code analysis tools for current and next-generation NVIDIA GPUs

Senior GPU Compiler Development Engineer

Platform Engineer

• Drive adoption of open source standard methodologies across product teams
• Design governance models covering ownership, decision-making, and contributor pathways
• Grow active contribution from external contributors
• Provision and operate community infrastructure including Slack, community meetings, and collaboration spaces
• Develop and run 30/60/90-day post-launch plans for newly open source repositories
• Build isolated, contributor-safe CI for external pull requests
• Create the end-to-end contributor path from starter issue through contribution guide and automated review
• Implement PR quality-signal screening before maintainer review
• Monitor community health, engagement, events, and program metrics
• Turn readiness engagements into reusable playbooks

Growth Engineer, Developer Platform

DevOps Engineer

• Develop, maintain, and optimize continuous integration and continuous deployment (CI/CD) pipelines for embedded software
• Implement and champion DevOps and Software Integration best practices across the team and broader organization
• Monitor builds in CI systems and anticipate, debug, and resolve build and test breaks
• Design and develop automation tools for build, test, and release workflows
• Manage and improve containerized build environments using Docker
• Implement new build configurations based on evolving project requirements
• Collaborate with development teams to provide efficient builds, tools, and infrastructure

DevOps Engineer – Build, CI/CD

General

• Creating and maintaining SKILL, Wiki, and agent harness
• Develop TileGym, Triton CUDA TileIR backend and CUDA Tile
• Develop highly optimized deep learning kernels through tile-based GPU programming model
• Perform end-to-end performance optimization through tile-based GPU programming model
• Do performance optimization, analysis, and tuning

Deep Learning Performance Software Intern

Engineering Manager

• Own the DOCA Networking Data Plane team
• Engineer industry-leading Virtual Switches
• Manage a team of senior engineers
• Address modern networking challenges across high-density AI (E/W) and external (N/S) workloads
• Leverage the NVIDIA DOCA SDK and SmartNIC architecture to achieve orders-of-magnitude speedups while maintaining full software flexibility
• Manage the team personally and professionally, focusing on research and development of networking features
• Mentor and lead team members
• Work hands-on to engineer next-generation networking technology
• Work with world-class technical leaders in the field

Software Manager – DOCA Services

Software Engineer

• Research and develop techniques to GPU-accelerate leading applications in high performance computing fields within scientific computing, computational engineering, and data science
• Perform in-depth analysis and optimization for current and next-generation GPU architectures
• Work directly with key application developers to understand current and future problems
• Craft and optimize core parallel algorithms and data structures using GPUs
• Contribute through library development and direct application contributions
• Collaborate with NVIDIA architecture, research, libraries, tools, and system software teams
• Investigate impacts on application performance and developer productivity to influence next-generation architectures, software platforms, and programming models

Developer Technology Engineering Intern – Compute Performance

Solutions Engineer

• Serve as the primary technical expert between NVIDIA and its ecosystem
• Partner externally with developers, researchers, technology specialists, IT professionals, and executives to facilitate integration of NVIDIA technology
• Partner internally with engineering, product, and research teams
• Help NVIDIA customers make their solutions more efficient, usable, or economical by driving adoption of state-of-the-art compute and software platforms

Solutions Architect, GenAI

Data Engineer

• Define requirements and acceptance criteria for the platform's flagship products: single-source-of-truth tables for clusters, associated components, customers, and workloads, along with relationship tables
• Partner with source and collector teams across DGX Cloud to onboard data and define versioned data contracts covering schema, semantics, freshness, quality, and change notification
• Drive identifier and schema standardization upstream with producing teams, recording, owning, and tracking each standardization decision to closure
• Work with infrastructure security, compliance, and enterprise data teams to define the security model
• Engage with engineering leadership to establish priorities and resource allocation, sequence delivery against firm external constraints, and gain prioritization consensus across collaborating teams
• Define and report measures including standardization debt per source, pipeline freshness and quality SLOs, source onboarding throughput, and time to first answer for a new consumer
• Define requirements and build the case for extending the platform to NVIDIA Cloud Partners and cloud service providers

Senior Data Platform and Analytics Product Manager – DGX Cloud

Blockchain Engineer

• Contribute to the DGX Cloud team responsible for production systems enabling large-scale GPU clusters for AI workloads
• Develop custom software for scheduling GPU resources on Kubernetes
• Implement monitoring and health-management capabilities for reliability, availability, and scalability of GPU assets
• Harness data streams from GPU hardware diagnostics, cluster telemetry, and network telemetry
• Collaborate with teams across NVIDIA to ensure production AI clusters run reliably, consistently, and with maximum performance
• Evaluate system failures and improve services through a defined incident-management process