NVIDIA

Program Manager

• Define and manage program schedules, deliverables, and turning points aligned with customers and partners roadmaps and requirements.
• Translate customer requirements into actionable tasks for all functional teams, driving timely issue resolution across engineering, quality, logistics, and sales.
• Provide horizontal leadership to drive cross-functional program execution, managing high priority issues/concerns, and coordinating globally dispersed teams to ensure clear alignment on objectives.
• Act as the primary customer interface, facilitating program kick-offs, technical discussions, design reviews, issues/bug tracking and resolution, customer qualification/validation efforts, and status updates throughout the product lifecycle.
• Drive program execution from design through production deployment, ensuring on-time delivery, quality, and customer acceptance.
• Provide ongoing post-deployment sustaining support, serving as case manager for field quality issues or high priority concerns with major enterprise customers, cloud service providers.
• Monitor factory production schedules, yields, and blockers, collaborating with ODM/contract manufacturers to resolve issues and meet operational targets with major enterprise customers, cloud service providers.
• Proactively communicate program health, risks, and key issues to both customers, internal partners, and leadership (Hardware, Software, Operations, Business, Quality, Sales, and Product Teams), ensuring transparency and alignment.

Senior Customer Program Manager – OEM

Systems Engineer

• Develop and maintain infrastructure and tools that automate software build, test, and deployment workflows.
• Build and improve performance measurement and benchmarking systems for large-scale software and hardware platforms.
• Collaborate with engineering teams to understand requirements and deliver reliable, scalable solutions.
• Analyze system performance and logs to identify issues and contribute to continuous improvements.
• Participate in design discussions and help improve platform automation and engineering efficiency.

System Software Engineer, AI Data Platform

Full-stack Engineer

• Design and develop safety (ISO26262) and security (ISO21434) compliant system software for NVIDIA DRIVE platform for autonomous vehicles
• Perform development & bring-up activities for next-generation Tegra SOC hardware
• Architect and Design Software for different OS flavours where by SW is Unified and Scalable on DRIVE platform
• Architect and Implement solutions to sophisticated issues & defects involving complex interactions between components partitioned across the large DRIVE Platform software stack

Senior Software Engineer – DRIVE SW

Mechanical Engineer

• Develop and maintain the electro-optic co-simulation flow
• Define silicon-photonics test structures and characterization plans
• Build and calibrate compact/behavioral models for silicon photonic components
• Correlate models against foundry and in-house characterization data
• Partner closely with circuit/SerDes designers, system teams, and foundry contacts

Senior Silicon Photonics Design Engineer

DevOps Engineer

• You will be working closely with compiler development and verification engineers across the globe on tasks related to: Improving automated build pipelines and processes, of compiler system software for NVIDIA processors.
• Developing DevOps tools and workflows, using Nvidia proprietary and other innovative technologies to help build better CI/CD workflows.
• Crafting efficiency and usability improvements across Nvidia proprietary products that will help streamline various release workflows across Nvidia.
• Be in the critical path supporting thousands of developers as well as intimately understanding the values of predictability, automation and reliability.
• Propose enhancements based on safety, compliance and other requirements.

Build and DevOps Engineer – Compilers

• Play meaningful role in NVIDIA's effort in contributing to JAX.
• Design and implement JAX core components and drive peak performance on NVIDIA products.
• Work with AI applied researchers and leaders to build future-proof models
• Build tools that will increase the efficiency of teams developing AI-based systems.
• Work to bridge the gap between the latest in numerical computing, simulation and deep learning research and their applications in real world products.

Senior Software Engineer, JAX

Product Manager

• Represent Israel
• Engage NBU and IL business stakeholders, IL engineering leaders, and end users to surface workflows, friction points, and AI opportunities unique to this site
• Influence broader IT Product priorities so IL needs are designed in
• Co-own delivery with the ~20-person IL engineering org driving NVBugs consolidation
• Own UX, backend behavior, integration design, rollout, and metrics end-to-end
• Set adoption, engagement, and productivity metrics that anchor IL's contribution to the global plan

Senior Product Manager – Internal AI, Employee Productivity

• Design, implement, and maintain full-stack features across frontend, backend services, and data layers.
• Own delivery of well-scoped components or features, from design through implementation, testing, and production support.
• Contribute to system reliability, performance, and observability improvements by identifying bottlenecks, fixing issues, and following established best practices.
• Collaborate with product managers, designers, and AI research stakeholders to understand user needs and translate them into clear technical solutions.
• Participate in design reviews, code reviews, and on-call rotations to help maintain a high-quality, production-ready system.
• Follow and contribute to team standards for code quality, testing, security, and CI/CD.
• Learn and apply modern practices in cloud infrastructure, deployment, and monitoring with guidance from senior engineers.

Senior Full-Stack Software Engineer

• Working on CUDA Tile, a new tile-based programming model for GPUs.
• Designing and implementing compiler transformations.
• Developing MLIR-based dialects and lowering passes.
• Optimizing the performance of tile-based kernels.
• Defining public APIs, crafting and implementing compiler and optimization techniques, performance optimization, and other software engineering work.

Senior DL Compiler Engineer – CUDA Tile

Infrastructure Engineer

• Design, build, deploy, and run internal tooling for large-scale AI training and inferencing platform built on top of cloud infrastructure.
• Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
• Engage in and improve the whole lifecycle of services—from inception and design through deployment, operation, and refinement.
• Support services before they become available through activities such as system design consulting, developing software tools, platforms, and frameworks, capacity management, and launch reviews.
• Maintain services once they are live by measuring and monitoring availability, latency, and overall system health.
• Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity.
• Practice sustainable incident response and blameless postmortems.
• Be part of an on-call rotation to support production systems.

Senior AI Infrastructure Engineer – DGX Cloud

Platform Engineer

• Lead design and development of cutting-edge end-to-end reference system stack for 5G/6G baseband system
• Optimizing CPU, GPU and NIC sub-systems for predictable low-latency and maximum throughput
• Collaborating with cross-functional teams to troubleshoot performance bottlenecks in CPU, GPU and NIC sub-systems
• Work in lab environment to troubleshoot and integrate complex software modules
• Develop software, implementing new baseband functions in C/C++/Python/CUDA on a GPU platform
• Support system integration, performance testing, system demonstration and lab trials for end-to-end system
• Be a technical bridge between engineering team and partners/customers engineering team
• Engage with customer field trials and technical teams
• Help in implementing missing features to unblock progress at customers/partners and in benchmarking and develop/evaluate test tools for Aerial SDK evaluation

Senior Software Engineer – Aerial Platform

AI Engineer

• Refine developer, user, and agent journeys: Understand how developers, enterprise platform teams, partners, and customers, and their respective agents, consume NVIDIA AI software, then craft clear technical journeys supported by documentation, code examples, demos, and deployment guidance.
• Showcase enterprise AI software workflows: Build demos, reference examples, notebooks, and sample applications that show how NVIDIA AI software components work together across model development, inference, RAG, agentic AI, evaluation, deployment, and operations.
• Build compelling technical assets: Accelerate adoption by creating public-facing content such as product documentation, deployment guides, reference architectures, tutorials, blog posts, whitepapers, technical presentations, webinars, demo videos, and code examples.
• Develop automation and docs-as-code workflows: Create repeatable examples and publishing workflows using Git-based documentation, CI/CD, scripts, templates, and AI-assisted docs or skills where appropriate.
• Enable the field and partner ecosystem: Support solution architects, sales teams, cloud partners, ISVs, and ecosystem teams with technical assets that help them explain, deploy, and integrate NVIDIA enterprise AI software.
• Collaborate across the stack: Work closely with Technical Marketing Engineering, Product Management, Engineering, Developer Relations, Field, and Marketing teams to turn product capabilities into practical adoption paths.
• Capture feedback and improve the product experience: Use customer, partner, developer, and field feedback to identify gaps in usability, examples, documentation, deployment patterns, and product workflows.
• Engage the developer and open source community: Advocate for NVIDIA AI software in developer, cloud-native, and open source ecosystems, encouraging adoption through clear examples and practical technical storytelling.

Senior Technical Marketing Engineer, Enterprise AI Software

• Create and implement the training infrastructure spanning pre-training, SFT, and RL post-training for Physical AI world foundation models
• Develop and improve the pre-training and SFT pipelines — large-scale data loading, distributed training, and checkpointing — to achieve high throughput and scalability
• Develop and improve the inference and evaluation stack, including the inference engine, inference/generation pipelines (which also support RL rollout), and evaluation pipelines
• Use methods like continuous batching and KV-cache management to achieve high throughput and low latency
• Build and improve the effective interaction and data flow among the RL system's roles (policy, rollout, reward, simulation) while investigating system-level optimization opportunities
• Integrate and orchestrate simulation and robotics environments as RL environments — driving the simulation↔rollout↔training loop at scale
• Build and refine the distributed training backend — sharding/parallelism, mixed precision, activation checkpointing, and memory/throughput optimization across many GPUs
• Improve the efficiency, scalability, and resiliency of training and RL workloads — focusing on fault tolerance, fast/elastic restart, and throughput optimization under preemption and hardware failure
• Define meaningful, actionable reliability and efficiency metrics to track and improve system reliability
• Root cause, triage, and resolve failures from the application level down to the framework, GPU, and network/hardware level

AI Infrastructure Software Engineer

Account Manager

• Build a partner capacity model aligned to industry verticals and regional priorities
• Identify, recruit, and onboard high-potential partners and SIs with the technical capability and enterprise relationships to scale AI Factory deployments
• Develop and complete joint business plans with key partners, including revenue targets, enablement achievements, and account mapping
• Collaborate with NVIDIA enterprise account teams to drive co-selling motions with partners into named enterprise accounts
• Drive partner technical enablement programs (in coordination with NVIDIA Solutions Architects) to build SI proficiency in AI Factory composition, deployment, and lifecycle management
• Support partner certification pathways and proficiency benchmarking
• Own partner-sourced and partner-influenced pipeline targets, forecasting, and conversion
• Maintain partner scorecards, report ecosystem health, coverage gaps, and growth plans to leadership

Senior Partner Business Manager

• Contribute to a platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers
• Build end-to-end automation of datacenter operations, break/fix, and lifecycle management for large-scale Machine Learning systems
• Implement monitoring and health management capabilities that enable reliability, availability, and scalability of GPU assets
• Manage NVLINK topography across GPU clusters
• Build automated test infrastructure to qualify distributed systems for operation
• Collaborate with engineering teams to ensure software integration from hardware to AI training applications