NVIDIA

Artificial Intelligence

• Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL)
• Explore distributed parallelism, sharding, low-precision training, compute–communication overlap, numerical consistency, and efficient weight synchronization between training and inference
• Build Physical AI post-training and RL infrastructure supporting advanced training algorithms
• Connect simulation or real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation
• Optimize workflows through partitioning, pipelining, data transfer, and synchronization across synchronous, asynchronous, or disaggregated execution
• Improve efficiency and scalability across training, inference, simulation, and evaluation through scheduling, placement, dynamic resource allocation, and load balancing
• Support heterogeneous resources, elasticity, and fault recovery
• Analyze and optimize system performance with researchers
• Investigate, support, and compare emerging Physical AI models, training workflows, and algorithms from a systems perspective
• Use profiling, benchmarking, and performance modeling to identify bottlenecks and measure throughput, latency, GPU utilization, and policy freshness
• Share findings through tested code, documentation, and technical presentations
• Contribute to research publications where appropriate

AI Infrastructure and Frameworks Intern

Engineer

• Work closely with developers to test new components, including crafting and executing unit, functional, and performance tests
• Develop verification environments and tools to qualify products from functional and performance perspectives
• Investigate performance-related issues in networking Linux drivers
• Analyze coverage measures to identify verification holes and show progress toward product development and releases
• Identify and write all types of coverage measures for stimulus and corner cases
• Incorporate AI mindset and practices in daily work
• Collaborate with NVIDIA Driver developers, verification teams, and performance architects
• Lead feature verification from design through implementation to integration into frameworks
• Develop robust infrastructure and work collaboratively with cross-functional teams

Software Performance Engineer

Cloud Engineer

• Craft RESTful cloud services and frameworks for GeForce NOW
• Create low-latency, highly available services supporting millions of gamers
• Design, build, and implement scalable cloud-based systems for SaaS/PaaS
• Help drive the underlying technology stack
• Collaborate with other teams on new products and feature improvements
• Drive performance tuning and automation
• Support, maintain, and document software functionality

Senior System Software Engineer, Cloud Services

Business Analyst

• Develop advanced planning solutions, data analysis, and optimization models for strategic and tactical supply chain planning
• Support existing SAP IBP/APO planning solutions
• Architect, design, and deliver solutions for complex supply chain requirements using SAP IBP Supply Planning, Demand Planning, Optimization, and Fulfillment Techniques
• Demonstrate process improvement techniques to streamline business processes and architect solutions
• Lead projects implementing new and improved IBP functionalities
• Build prototypes and proofs of concept for business requirements and translate them into operational solutions
• Apply industry best practices for data analysis and share them across teams
• Build business and product scenarios to improve NVIDIA’s profitability and market share
• Perform testing for system implementations and upgrades, and drive system design analysis, testing, and training
• Collaborate with cross-functional business and IT teams to deliver supply chain planning solutions
• Influence senior business leadership to adopt scalable solutions and systems/tools

Senior Business Systems Analyst, SAP IBP Planning

Full-stack Engineer

• Improve the performance, reliability, and scaling behavior of a system routing AI workloads onto distributed GPU fleets
• Design and ship services in Java, Go, and Rust
• Build in the open on a public repository with transparent commits, design proposals, and reviews
• Automate and optimize build, test, integration, and release processes for cloud native systems
• Partner with engineering teams across NVIDIA to integrate adjacent technologies, including KAI Scheduler, NVIDIA NIM, Grove, and Dynamo
• Triage community issues and pull requests for an open-source project
• Write documentation for contributors
• Develop, deploy, and monitor GPU- and DPU-accelerated applications on NVIDIA hardware

Senior Systems Software Engineer – NV Cloud Functions

Developer Relations

• Serve as a trusted technical advisor, problem solver, and champion for the Healthcare and Lifesciences developer ecosystem
• Drive adoption of NVIDIA technologies with multifunctional partners
• Demonstrate groundbreaking solutions integrating the NVIDIA stack into developer products, platforms, and pipelines
• Advise on technical enablement resources, including sample code, guides, demonstration pipelines, and tools
• Highlight applications including scientific reasoning, pharmaceutical clinical development, trial matching, regulatory submissions, ambient clinical documentation, and cross-specialty agent orchestration
• Guide customers, partners, and startups through onboarding and integration with NVIDIA programs
• Foster co-innovation and development of next-generation solutions
• Map, track, and monitor the European healthcare and life sciences ecosystem, particularly around agentic AI
• Identify growth opportunities, inform technology roadmaps, and shape adoption strategies
• Collaborate with solution architects, engineering, product management, and marketing to drive developer engagement and optimize partner adoption
• Engage partner engineering teams, technical leaders, and decision-makers to identify goals, solve technical challenges, and promote integration best practices
• Represent partner technical needs and feedback to NVIDIA product and engineering teams
• Supply actionable field-deployment insights to influence product roadmaps

Senior Developer Relations Manager – Healthcare AI

General

• Design and implement novel AI algorithms and models for general-purpose humanoid robots and embodied agents
• Develop large-scale AI training and inference methods for foundation models
• Optimize and deploy AI models in physical simulation and on robot hardware
• Collaborate with research and engineering teams across NVIDIA to transfer research to products and services

PhD Research Intern, Generalist Embodied Agents Research

• Develop groundbreaking technologies in machine learning compilers and AI systems
• Develop innovative AI compiler technologies for NVIDIA hardware architecture
• Develop new AI compiler abstractions
• Develop agentic solutions to accelerate machine learning system engineering
• Optimize high-impact machine learning workloads
• Innovate and develop new machine learning compiler and systems technologies
• Design, implement, and optimize compilers for high-impact AI workloads
• Build agent-driven solutions to automate the overall development flow
• Co-design agent system solutions with current and future AI compiler technologies
• Collaborate closely with other NVIDIA engineering teams to build high-impact solutions for machine learning acceleration

AI Compiler Engineer – New College Grad

• Own the end-to-end software integration strategy for products across firmware, BSP, drivers, GPU software, services, test, and release systems
• Lead system-level debugging of boot failures, hangs, crashes, unexpected resets, power-transition failures, performance regressions, update failures, and firmware/driver interactions
• Use crash dumps, event logs, traces, UART output, firmware logs, telemetry, and hardware diagnostics to isolate failures across system layers
• Build automated failure classification distinguishing product defects from infrastructure, configuration, device, test-harness, and flaky-test failures
• Drive root-cause analysis and corrective actions for escaped defects, integration failures, and recurring infrastructure problems
• Integrate OEM and partner software into the NVIDIA software stack to ensure compliance, stability, and power/performance benchmarks for production platforms
• Work closely with multifunctional teams to prioritize issues across firmware, drivers, kernel, and hardware layers
• Collaborate with architecture, silicon, firmware, and OS engineering teams to enable new features and ensure cross-component integration

Senior System Software Integration and Debug Engineer

Architect

• Investigate new architecture concepts for future GPUs, memory systems, accelerators, and data-center-scale computing platforms
• Study emerging workloads and identify architectural bottlenecks and opportunities
• Explore hardware–software co-design across architecture, compilers, runtime systems, programming models, and applications
• Develop models, simulators, prototypes, and experimental tools to evaluate architecture ideas
• Research approaches to memory hierarchy, coherence, consistency, data movement, and system-level programmability
• Collaborate with NVIDIA researchers, GPU architects, software teams, and product groups
• Communicate research findings through presentations, technical reports, and potentially research publications
• Help transfer successful research ideas, methodologies, and tools into NVIDIA product teams

PhD Research Intern, Architecture

3D Artist

• Design and develop Python and Qt/PySide tools, desktop applications, and DCC integrations
• Work directly with artists, technical artists, and engineers to understand production challenges and turn them into practical software solutions
• Improve existing tools and workflows with a focus on usability, reliability, performance, and iteration speed
• Develop integrations across DCC applications, production systems, and real-time environments
• Explore new approaches to content creation and workflow automation, including agentic AI systems that can assist with repetitive or multi-step production tasks

Senior VFX Artist

Technical Account Manager

• Work with NVIDIA enterprise customers to resolve sophisticated technical issues and customer concerns through research and reproduction
• Support customers installing products focused on next-generation AI and HPC server technologies
• Own and resolve customer issues during installation, operation, maintenance, or product application
• Work with GPUs, AI accelerators, high-speed interconnects, ML frameworks, Spark, Kubernetes, and Ceph
• Analyze, communicate, and solve problems to improve customer experiences
• Develop, redefine, and document standard methodologies for internal support and R&D teams
• Interact regularly with engineering, marketing, and support teams
• Serve as a main point of contact for customers and maintain high customer satisfaction

HPC Technical Account Manager

• Analyze the performance of application code running on NVIDIA GPUs with the aid of profiling tools
• Identify opportunities for performance improvements in the MLIR/LLVM-based compiler middle-end optimizer
• Identify and implement novel ideas in the compilation pipeline to enable best-in-class performance of AI workloads
• Design and develop new compiler passes and optimizations to produce robust, supportable compiler and tools
• Interact with the open-source LLVM community to ensure tighter integration
• Work with geographically distributed compiler, hardware, and application teams to oversee improvements and problem resolutions
• Contribute to deep-learning compiler technology spanning architecture design and support through higher-level languages

Senior Compiler Optimization Engineer – MLIR

• Develop accelerated, PyTorch-based solutions for large-scale machine learning models, including GNNs, TFMs, and ensemble models, with a focus on efficient training and inference on GPU infrastructure
• Support CUDA-X Libraries and integrations used in PyTorch-based, large-scale machine learning workflows
• Partner with developers, product managers, and scientists to develop innovative GNN models and GPU-accelerated implementations for model development and prediction phases
• Develop solutions that help customers adopt NVIDIA hardware and software, and gather technical requirements directly from customers and Solutions Architects to guide product and engineering priorities
• Provide technical leadership and mentorship to engineers across the team
• Identify opportunities to improve the codebase and reduce code-maintenance overhead through re-architecture
• Apply agentic coding tools to identify and fix bugs, implement new features, and refactor code
• Solve complex technical issues, explain solutions clearly, exercise technical leadership, and coordinate across multiple teams to achieve shared objectives

Senior Software Engineer, GNN

Manager

• Lead and manage a dynamic team of engineers, ensuring smooth and efficient project execution
• Develop and implement NVIDIA's Ethernet SDK with a focus on high performance and reliability
• Collaborate with teams from other subject areas to identify technical requirements and implement innovative solutions
• Follow established guidelines and standards to deliver high-quality products
• Continuously improve team performance and drive innovation