NVIDIA

Full-stack Engineer

• Design, develop and optimize server application responsible for managing NVLink networks of thousands of network devices!
• Collaborate with multiple teams in our multi-functional environment on developing new features and improvements
• Participate in the design and architecture of new next generation network solutions for super computers
• Manage issues during integration, testing, deployment & QA
• Work and collaborate with team members in different geographical locations

Senior Software Engineer, Subnet Management

Engineer

• Analyzing deep learning networks and developing compiler optimization algorithms
• Collaborating with deep learning framework and hardware architecture teams to accelerate software
• Defining public APIs and implementing compiler infrastructure techniques for neural networks

Senior Compiler Engineer – DL

• Design and implement Python-first APIs, SDK workflows, and plugin interfaces for building, measuring, and improving agents across multiple runtimes and product surfaces
• Build reusable systems for observing behavior, measuring progress, detecting regressions, and turning runtime evidence into product decisions
• Build systems for ingesting, normalizing, validating, and analyzing agent execution data and evaluation datasets
• Partner with research, product, platform, and infrastructure teams to integrate agentic capabilities broadly across NVIDIA agent runtimes and developer workflows
• Help turn emerging agent development and improvement techniques into reliable, reusable product capabilities
• Improve reliability, observability, debuggability, and performance across NeMoStack services, SDKs, plugins, jobs, and developer workflows
• Build strong test coverage across unit, integration, E2E, Docker, and Kubernetes workflows
• Drive 'speed of light' engineering: fast iteration, high ownership, pragmatic decisions, and performance-minded implementation under production constraints
• Provide senior technical leadership through design reviews, code reviews, mentoring, and ownership of ambiguous cross-component problems

Senior Software Engineer, Agentic Systems

Operations

• Establish the site’s operating model, mission, and performance rhythm to align with NVIDIA’s broader manufacturing and supply objectives.
• Partner to lead day-to-day site operations, coordinating people, resources, and factory interfaces to ensure build plans are accomplished, and supply commitments are protected.
• Serve as the primary NVIDIA contact for Houston-area manufacturing partners, addressing operational and business issues in a structured and timely manner.
• Provide leadership during factory disruptions, emergency response situations, and recovery efforts, ensuring clear communication, addressing critical issues, and restoring stable operations.
• Build and develop the Texas operations team, including workforce planning, hiring, mentoring, and performance management.
• Foster a culture grounded in accountability, collaboration, and continuous improvement.
• Partner with internal functions across planning, engineering, quality, materials, IT and logistics to align working models, tools, important metrics, and resource plans.
• Act as a senior point of contact when cross-functional alignment is required.
• Coordinate continuity of supply assurance processes, including forecast acceptance alignment and execution through delivery of finished goods into NVIDIA-managed distribution and customer-owned hubs.
• Manage site-level budgets and resources responsibly, ensuring operational investments support performance, resiliency, and long-term scalability.
• Provide regular, clear reporting on operational performance, risks, and recovery actions to senior leadership.

Senior Manager, Manufacturing Operations

Solutions Engineer

• Collaborate with other teams to plan and implement data center infrastructure solutions based on NVIDIA Datacenter reference architecture, including power distribution, cooling systems, network architecture, server hardware, and storage systems.
• Plan and manage deployment of NVIDIA's pioneering AI infrastructure solutions including highly complex rack-scale, liquid cooled compute and networking hardware systems, in a fluid and fast paced environment.
• Conduct pre-deployment planning including reviewing cluster and data center architecture, plan network port mapping and fiber optic cabling BOM, identify potential risks, train vendors and find areas for improvement.
• Evaluate customers' and partners' infrastructure design proposals for consistency with industry standards and regulatory requirements.
• Provide feedback and recommendations to improve performance, scalability, and cost-effectiveness.
• Perform testing, troubleshooting and validation of compute systems based on collaboration with product and engineering teams.
• Act as the NVIS mentor providing guidance, mentorship, and support to ensure the NVIS team's success in their respective roles.
• Establish and enforce quality assurance processes to verify that deployments meet established specifications and performance benchmarks.
• Conduct thorough bring-up, testing, and validation to validate the functionality and reliability of infrastructure components.
• Drive continuous improvement initiatives to enhance data center infrastructure efficiency for NVIDIA data center reference architecture and deployment blueprint, resilience, and sustainability.
• Find opportunities to streamline processes, automate repetitive tasks, and leverage emerging technologies to optimize infrastructure operations.

Solutions Architect, Data Center Infrastructure

Technical Program Manager

• Ensuring that from engineering sample to mass production, that NVIDIA is ready to support with tooling, processes and lessons learned which are implemented and available for internal and external roll out.
• Program management of the status for hardware, software, tooling, and process are defined.
• Lead customer engagement and communication, coordinating planning, and activity preparation.
• Quality Assurance: Establish and implement quality assurance processes to verify that deliverables meet established specifications and quality requirements.
• Continuous Improvement: Drive initiatives to improve efficiency through automation, tooling, and other means to increase speed and accuracy of readiness planning.
• Partnership and Communication: Collaborate and communicate across internal teams and customers to facilitate the seamless integration of our readiness capabilities.

Technical Program Manager – New Product Readiness

• Design and develop innovative AI-driven web products to solve big data challenges in chip design.
• Build high performance API's, data pipelines and backend services in a distributed systems environment.
• Own technical strategy for broad and complex challenges.
• Collaborate closely with product and engineering teams to translate high-level requirements into actionable deliverables.
• Handle multiple tasks and adapt to changing priorities.
• Be an engineering generalist.
• Discover and build skills needed at different times to solve the problems at hand.

Senior Software Engineer, AI Web Products

DevOps Engineer

• Working on building tools to improve the SRE Observability.
• Be part of the Kubernetes migration journey with VMI setup and problem solving.
• Rapidly debug and triage incidents and user-reported issues
• Taking ownership of automating, scripting, and tooling of new/existing scripts to help the team achieve 100% automation of daily tasks
• Support services before they go live through activities such as system design consulting, developing software platforms and frameworks, capacity management and launch reviews.
• Be part of an on call rotation to support production systems

Senior Site Reliability Engineer, GeForce NOW

Architect

• Own system architecture for inference stack and other applications running on this class of products
• Co-architect with the orbital hardware system architecture team
• Define the manageability architecture for an unreachable, autonomous data center
• Architect rad-tolerant system software behaviors
• Drive Redfish, MCTP, PLDM, and constellation-level management protocols
• Partner with cloud and constellation customers to translate mission requirements
• Drive reliability and optimization in the system software architecture
• Own quality, reliability, and telemetry performance of the system software

Principal Architect, System Software

Machine Learning Engineer

• Lead the strategic direction, development, and continuous improvement of the AI/ML platform
• Optimize efficiency and resilience of different stages of ML workflow
• Lead and mentor a team of highly skilled engineers
• Work closely with various internal teams to understand their needs
• Drive innovation and efficiency within the platform
• Maintain a strong customer focus

Senior Manager, Artificial Intelligence – Machine Learning Platform

Security Engineer

• Lead day-to-day Cyber Defense & Response (CDR) functions, ensuring operational readiness, execution rigor, and alignment with NVIDIA’s cybersecurity strategy.
• Responsible for the incident response lifecycle from start to finish.
• Act as the point person for critical incidents and lead efforts to investigate, contain, recover, and review the incident afterward.
• Increase detection and response efficiency by continuously broadening coverage across on-premises and multi-cloud environments, targeting GPU-accelerated and AI/ML workloads.
• Set detection expectations and standards aligned to NVIDIA’s threat landscape, defining monitoring requirements, coverage goals, and compliance requirements.
• Own operational performance and outcomes, including MTTD, MTTC, MTTR, detection coverage, alert quality, and false-positive reduction—focused on root-cause improvement.
• Run CDR improvements following agile methods, keeping a risk-prioritized backlog and guiding delivery via planning, reviews, and retrospectives.
• Drive agentic transformation of manual SOC work — identify repetitive analyst tasks and convert them into automated, event-based AI pipelines.
• Partner with Security Engineering to operationalize tooling, automation, playbooks, and post-incident improvements that measurably improve response efficiency.

Manager, Cybersecurity

QA Engineer

• Driving quality improvements through meticulous supplier assessments and audits.
• Collaborating closely with suppliers to ensure compliance with NVIDIA's extraordinary standards.
• Developing and implementing supplier quality metrics to manage performance.
• Taking charge of root cause analysis and corrective action processes to address any quality concerns or issues.
• Working with multi-functional engineering teams to resolve and implement quality requirements.
• Offering mentorship, training and support to suppliers to improve their performance.
• Ensuring strict consistency to quality control procedures and regulations.

Senior Supplier Quality Engineer

• Own end-to-end Windows enablement for DGX Station—driving the platform from initial bring-up on Windows through WHQL certification to customer-ready shipping quality
• Drive Linux bring-up and continuous enablement for DGX Station on DGX OS / Ubuntu
• Enable and validate BIOS/UEFI, BMC, and system-level firmware for Windows and Linux on the Grace (Arm) + Blackwell GB300 architecture
• Coordinate GPU driver, display driver, and compute driver bring-up and validation on Windows (WDDM, MCDM) and Linux (open-gpu-kernel-modules, DRM/KMS)
• Ensure the CUDA toolkit, cuDNN, TensorRT, NCCL, and NVIDIA’s AI SDK stack are fully functional on DGX Station on both Windows and Linux
• Validate that NVIDIA AI applications run correctly on DGX Station across Windows and Linux
• Drive the overall test strategy for DGX Station on Windows and Linux, including functional testing, stress testing, and reliability

Senior Systems Software Engineer – Windows and Linux Enablement

• Develop core components of the CUTLASS platform including Tensor Core MMAs, copies, synchronization barriers, schedulers, and other GPU hardware features in CUDA C++ and CUTLASS Python DSL.
• Contribute to the advancement of the MLIR-based backend compiler stack for the CUTLASS Python DSL by designing dialects and associated compiler passes.
• Author example kernels utilizing CUTLASS abstractions to showcase the use of novel GPU hardware features that are crucial for achieving high performance.
• Collaborate with GPU architecture, CUDA, and NVVM/PTX compiler teams to provide feedback on programming models and to assess the performance of future GPU hardware features.

Senior Software Engineer, CUTLASS Platform

• Benchmark the performance of state-of-the-art deep learning models’ inference and training passes to identify key GPU kernel and fusion opportunities.
• Identify gaps between theoretical and realized performance, and suggest software improvements or model adjustments to resolve them.
• Develop tooling to automate the benchmarking, analysis, and performance optimization loop to push the limit of CUTLASS kernel performance within DL networks.
• Be the authoritative resource on kernel performance in the team and engage with teams across NVIDIA including GPU architecture, DL frameworks, and QA as the performance representative for the CUTLASS team.