NVIDIA

Full-stack Engineer

• Implement features and services across the stack (back‑end, cloud, tooling), writing clean, maintainable code and contributing to design discussions.
• Help build and iterate on AI‑assisted development workflows (e.g., code generation, refactoring, automation in CI/CD), and experiment with powerful coding agents in real engineering use cases.
• Develop and operate services on Kubernetes using Helm and major cloud provider APIs (e.g., AWS, Azure, GCP), including deployment, monitoring, and troubleshooting.
• Collaborate with robotics and simulation teams to integrate services and tools into robotics workflows; learn and, where applicable, extend platforms such as Isaac or ROS.
• Follow good engineering practices around testing, code review, documentation, observability, and reliability, and contribute to improving them over time.
• Work closely with teammates across cloud, AI, and robotics, ask questions, share knowledge, and continuously expand your skills.

Senior Software Engineer – Omniverse

Mechanical Engineer

• Drive Physical Design Implementation for Nvidia’s GPUs, CPUs, and LPUs at block level, cluster level, and full chip level.
• Lead Timing closure and PPA convergence of complex IPs by analyzing RTL/Design, evaluating critical datapaths, and developing and implementing innovative solutions to improve QoR and adopting AI/Automation.
• Collaborate with Cross-Functional Teams to synthesize and optimize designs, devise timing closure strategies, create timing constraints, and drive timing, area, and power convergence

Senior Processor Physical Design Engineer

Artificial Intelligence

• Develop MLIR-based graph representations and optimizations for future GPU architectures
• Partner with framework and hardware teams to enable new model patterns and upcoming GPU architectural features
• Define APIs and MLIR dialects, conduct performance optimizations and analysis, implement compiler optimizations and kernel generation for neural networks
• Contribute to other general software engineering work

Senior AI Compiler Engineer, MLIR

Solutions Engineer

• Help customers with their AI factory journey, including workflow pipelines and performance optimization
• Focus data center implementations for inference use cases, including distributed, disaggregated and scaled out workflows
• Scope physical AI journeys on Omniverse, including synthetic data generation, data aggregation, application development and simulation pipelines
• Lead technical sales activities for AI factories with focus on hybrid deployments between cloud and on-prem
• Deliver hybrid cloud architectures for data pipelines, storage, security and user streaming connectivity
• Providing expertise in infrastructure workflows, including hardware, coordination of workload processes, and application tuning
• Understand different solutions trade-offs and propose enterprise customers the best architecture and technical execution
• Work directly with key customers to understand workflows and share feedback with internal product and engineering teams

Senior Solutions Architect, AI Factory Infrastructure

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Senior Solutions Architect, Retail

• Help customers with their AI factory journey, including workflow pipelines and performance optimization
• Focus data center implementations for inference use cases, including distributed, disaggregated and scaled out workflows
• Scope physical AI journeys on Omniverse, including synthetic data generation, data aggregation, application development and simulation pipelines
• Lead technical sales activities for AI factories with focus on hybrid deployments between cloud and on-prem
• Deliver hybrid cloud architectures for data pipelines, storage, security and user streaming connectivity
• Providing expertise in infrastructure workflows, including hardware, task coordination and application tuning
• Understand different solutions trade-offs and propose enterprise customers the best architecture and technical execution
• Work directly with key customers to understand workflows and share feedback with internal product and engineering teams

Solutions Architect, AI Factory Infrastructure

Research Scientist

• Serving as a technical lead for the development of datasets, tasks, and evaluation frameworks for foundation models
• Building external collaborations and community efforts to build high-quality datasets and biologically meaningful tasks for complex problems in digital biology
• Establishing benchmarks and evaluation criteria, and demonstrating the strengths of our research output to promote tangible progress in the field
• Inventing novel training and inference techniques, harnessing the power of NVIDIA accelerated computing platforms
• Publishing original research and releasing open-source software
• Collaborating with research and engineering teams across NVIDIA to translate research into products and services
• Mentoring emerging researchers and helping set the technical direction of the project

Senior Research Scientist, Digital Biology

• Serve as a senior technical authority and trusted consultant on NVIDIA technologies
• Establish and refine monitoring and optimization methodologies
• Lead and advise on the analysis, optimization, and performance tuning of complex GPU-accelerated systems
• Facilitate post-deployment reviews and incident retrospectives
• Own and lead complex technical projects end-to-end
• Support business growth by identifying AI infrastructure opportunities

Senior Solutions Architect, Customer Success

Manager

• Be an effective leader in crafting and driving a delivery of high quality SW in an efficient way.
• Partner with Engineering, Operations, Architecture and Marketing to coordinate the release of the products and ensure release will meet the schedule and with high quality
• Take full ownership on the relevant products from the kickoff to signoff
• Work with planning and tracking systems to lead the release progress, and build release indicators
• Partner with the QA teams to ensure alignment of all tests between the development, verification and QA groups
• Conduct and Attend release meeting and communicate release status.
• Resolve issues when they appear and raise red-flags when needed
• Collaborate with teams across the globe to drive process changes that can help improve development and release workflow
• Improve process, guidelines, checklists across NCS group

Senior Software Release Manager, Networking Clusters

• Developing a highly optimized inference framework running on the world’s largest supercomputers and data centers
• Collaborating in a dynamic and challenging work environment on innovative, next-generation products at the forefront of technology

Senior Software Developer

Engineer

• End-to-End Stack Readiness: Ensure every DGX platform is ready for the full NVIDIA software stack—firmware, DGX OS, GPU drivers, CUDA toolkit, DCGM, DOCA/OFED, and management tools—as a validated, production-quality product.
• Platform Firmware Development: Lead development of the manageability firmware stack (BMC, BIOS) for all DGX platforms.
• Validation Strategy: Define validation strategy proving each DGX platform is production-ready: end-to-end system validation including firmware regression, NVQual certification, DL workload performance, OS/CUDA stack testing, multi-user scenarios, power/thermal validation, and field upgrade reliability.
• Platform Bring-Up & Architecture: Drive platform bring-up for each new DGX system—coordinating first boot across new silicon (CPU, GPU), board design, and firmware teams.
• Customer Deployment & Enablement: Ensure firmware release flows meet CSP and enterprise deployment requirements.
• Product Delivery Lifecycle: Own the complete DGX delivery lifecycle—system architecture, firmware development, integration, full-stack validation, GA release, and customer deployment—for every DGX product.
• Cross-Org Alignment: Serve as single point of accountability for DGX platform readiness across NVIDIA—aligning GPU, CPU, networking, security, OS, and AI software teams to deliver on schedule.
• Quality & Vendor Management: Own RCCA processes for field issues.
• Team Leadership: Build and lead a world-class engineering organization.

Manager, Distinguished Engineer – DGX Systems Software

• Integrate TensorRT-LLM for BioNeMo models.
• Optimize models for low-latency, high-throughput inference.
• Profile and debug deep learning workloads on GPUs.
• Develop and validate custom GPU kernels for hot paths.
• Collaborate with research to align model architecture and training.

Senior Deep Learning Algorithms Engineer

• Assist in supporting industry accounts and driving research/influencing/new business in those accounts.
• Assist researchers/engineers on their GPU applications.
• Deliver technical projects, demos and client support tasks as directed by the Solution Architecture leadership team.
• Provide technical support for GPU system deployments.
• Be an industry thought leader on integrating NVIDIA technology into applications built on Deep Learning, High Performance Data Analytics, Robotics, Signal Processing and other key applications.
• Be an internal champion for Data Analytics, Machine Learning, and Cyber among the NVIDIA technical community.

Deep Learning Solution Architect

• Support Business Development and Sales teams as part of a Team of 4, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive our developers’ ecosystem success.
• Work directly with developers and customers in a customer-facing setting
• Support developers in adopting NVIDIA libraries and software frameworks as the foundation for modern AI and data platforms.
• Analyze application architectures and find opportunities for acceleration.
• Provide feedback and collaborate with engineering, product and research teams.
• Deliver trainings, hackathons and technical demonstrations on NVIDIA solutions and platforms.

Senior AI Solutions Architect

Developer Relations

• Serve as the trusted technical advisor, problem solver, and champion for the developer ecosystem in the AI and Agentic Security industry with cross-functional partners to drive adoption of NVIDIA technologies.
• Accelerate critical workloads by demonstrating groundbreaking solutions that integrate the core NVIDIA stack into developer products, platforms, and pipelines.
• Guide partners and startups through onboarding and integration with NVIDIA’s programs, fostering co-innovation and the development of next-generation solutions.
• Map, track, and monitor the developer ecosystem to identify growth opportunities, inform technology roadmaps, and shape adoption strategies.
• Collaborate multi-functionally with solution architects, engineering, product management, and marketing to drive developer engagement and optimize partner adoption strategies.
• Represent and advocate for the partner technical needs and feedback to NVIDIA’s internal product and engineering teams, supplying actionable insights from field deployments to influence product roadmaps.