NVIDIA

Full-stack Engineer

• Write Tensor Core-based deep learning kernels such as grouped-GEMM, attention, and convolution using CUTLASS CUDA C++ and Python DSL for Blackwell, Rubin, and future architectures.
• Optimize kernels for peak throughput on both silicon and software performance simulators.
• Collaborate with teams across NVIDIA including the GPU architecture, NVVM/PTX compiler, CUDA library, and DL frameworks teams to ensure fast, functional, and timely kernel delivery to customers.

Senior Software Engineer, CUTLASS Kernels

Architect

• contribute to features that help next-generation GPUs advance the state of AI
• keep up with the latest DL research and collaborate with diverse teams (internal and external to NVIDIA)
• analyze the behavior of various deep learning methods
• propose new features to accelerate or enable various methods
• study the benefits of the proposed features

Deep Learning Computer Architect

• Develop and implement CUDA Core Libraries in C++ and/or Python, including parallel algorithms and idiomatic language bindings for core CUDA functionality
• Compose, optimize, and evolve GPU algorithms and APIs, from high-level interfaces down to low-level performance tuning involving memory, parallelism, and synchronization
• Own features end-to-end: develop, implementation, testing, benchmarking, documentation, and long-term maintenance
• Improve developer experience across the stack: CI, tests, benchmarks, packaging, examples, and docs
• Collaborate with senior CUDA engineers in design reviews, code reviews, and open-source-style workflows
• Engage with real users through issues, performance investigations, and API feedback

Senior Software Engineer, CUDA Core Libraries

• Own production readiness of AI applications on DGX Station—NemoClaw, Hermes agents, NIM microservices, and key customer workloads.
• Define “ready to ship” criteria, run validation, and close every gap between “it runs” and “it runs well” across single-GPU and multi-GPU configurations.
• Work cross functionally with different orgs to profile and optimize LLM and deep learning workloads (PyTorch, TensorFlow, JAX) across training and inference on the GB300 Blackwell multi-GPU architecture.
• Characterize performance across model sizes, batch sizes, precision modes (FP16, INT8, FP8), and GPU scaling (single-GPU vs. multi-GPU with NVLink) to establish benchmarks and identify regression.
• Identify bottlenecks in GPU compute, NVLink bandwidth, host memory, PCIe, and CPU–GPU communication.
• Implement or drive optimizations across the stack: kernel tuning, memory placement, NVLink utilization, data pipeline efficiency, and scheduling to increase throughput on DGX Station’s multi-GPU topology.
• Work with NVIDIA’s framework, compiler (TensorRT, NVCC, Triton), and GPU architecture teams to improve kernel fusion, graph execution, operator scheduling, and memory management for Blackwell GPUs.
• Validate multi-user and concurrent workload scenarios—multiple users running simultaneous training jobs, inference serving alongside development, and resource isolation via MIG or time-slicing.
• Validate the full NVIDIA AI software stack on DGX Station: CUDA toolkit, cuDNN, TensorRT, NCCL, Triton Inference Server, DCGM, and DOCA/OFED.
• Build and maintain performance benchmarking infrastructure for DGX Station—automated regression tracking across key models (LLaMA, GPT, Stable Diffusion, Whisper), framework versions, and driver updates.

Senior Systems Software Engineer, AI Stack, Performance

Product Manager

• Define, own, and drive the strategy enabling the world’s enterprises to adopt accelerated query engines powered by NVIDIA’s platform
• Engage deeply with customers, ISVs, storage partners, and CSPs to find opportunities to improve their experience, owning the voice of the customer and incorporating insights into hardware and software roadmaps and priorities
• Collaboratively develop and lead enterprise go-to-market motions, working with a cross-functional team on asset creation, market research, positioning, enablement, etc.
• Track and act on important measures for adoption, engagement, and customer success to continuously improve our value proposition and developer experience
• Serve as the point person for presenting vision, opportunities, and progress, setting expectations and managing internal and external collaborators

Senior Product Manager – Structured Data Query Engines

Machine Learning Engineer

• Continuously keeping up to date on the latest advancements in generative AI research.
• Analyzing and prototyping emerging workloads in multi-agent AI systems, generative multimodal models, and inference-time compute scaling.
• Pioneering and developing optimizations for these workloads across the inference stack to push the boundaries of inferencing quality and speed on NVIDIA systems.
• Collaborating closely with production teams to incorporate the latest advancements into cutting-edge software frameworks.

Senior Deep Learning Engineer

• Take full responsibility for the strategic direction and roadmap of the break-fix automation system spanning multiple vendors, technologies, and CSPs.
• Define automation confidence thresholds, blocking issue criteria, and human-in-the-loop intervention points that balance speed with operational safety.
• Build the operator UX for repair queues, workflow transparency, and audit trails — ensuring on-call engineers have the context they need to act quickly and confidently.
• Drive the integration between failure attribution and automated repair actions, following through from detection to resolution.
• Define repair SLOs and own the metrics framework for time-to-drain, time-to-healthy, and overall fleet availability.
• Collaborate with NCP operators, SRE teams, and hardware vendor partners to integrate RMA processes and optimize repair workflows at scale.

Senior Product Manager, AI Factory Infra

Engineer

• Build performance modeling and prediction tools for AI workloads at Data-center scale
• Develop production tools and workflows used by multiple teams both within NVIDIA and its customers
• Automate workflows including search for the most efficient configurations over millions of parameters
• Partner with HW and SW architects to propose new features or improve existing features with real world use cases

Senior Datacenter Performance Model Engineer

Data Analyst

• Manage and optimize GPU capacity and other compute resources across various cloud service providers
• Build, develop, and maintain data models, reporting systems, data automation systems, dashboards, and performance metrics
• Analyze the technical and business needs for GPU capacity and other compute resources
• Identify performance bottlenecks in day-to-day usage of compute resources and collaborate to resolve them
• Drive infrastructure resource efficiency initiatives in partnership with engineering, finance, and product teams
• Develop and enhance tooling for our cloud infrastructure and analytics platform to optimize resource usage and performance
• Partner and cross-collaborate with Finance, Product, Service Owners, and Infrastructure Engineering teams

Capacity Operations and Analytics Manager

• Design and implement next-generation NVLink features in C/C++.
• Support the bring up of new NVLink platforms from Pre-Silicon to Production
• Perform verification, validation, and testing of NVLink-connected products using various methods and tools.
• Debug and resolve any issues related to NVLink and provide technical support to customers and partners.

Senior System Software Engineer

Frontend Engineer

• Build and implement critical features for core NGC services, including Identity and Access Management, user and group management, account security and credentials, and billing and subscription flows.
• Play a key role in developing the unified portal that acts as the gateway for all NVIDIA cloud products.
• Proactively apply AI tools and agents to boost code quality, speed up development, and increase overall system stability.
• Drive the evolution of our application architecture and development patterns for increasingly large and complex components.
• Work closely with Product, UX, and Backend teams to convert complex requirements into elegant, responsive UIs and satisfying user experiences.
• Participate in all stages of the product lifecycle, including planning, documentation, code review, and offering helpful insights.
• Estimate personal timelines and coordinate work to deliver achievements effectively.
• Present and/or demonstrate your work/research in front of colleagues from across the company.

Frontend Web Software Engineer

Artificial Intelligence

• As a core contributor to the CUTLASS project, you will use your expertise in systems programming and API design to create a world-class developer experience for GPU programming and kernel delivery
• Design APIs that prioritize user productivity, providing a "native" feel for developers accustomed to modern scientific computing and deep learning frameworks
• Develop robust compilation infrastructure—including AST transformations and JIT-friendly execution—to lower Pythonic descriptions into high-performance GPU machine code
• Optimize developer experience by creating debugging tools, profiler integrations, and validation methodologies that make writing and using kernels easy
• Build production-grade delivery infrastructure for the open-source community, managing everything from package distribution (wheels, conda) to the user-facing documentation and testing

Senior AI Frameworks Engineer

Solutions Engineer

• Own the creation of scalable datacenter solutions for enterprise AI/ML systems
• Craft detailed requirements for pioneering infrastructure patterns and datacenter wide architectures
• Create and validate cluster designs, optimizing them for enterprise facilities
• Collaborate closely with other experts in compute, software, and storage to drive innovation
• Lead multi-disciplinary projects, addressing high-level goals and complex challenges
• Engineer on-premises cloud-native solutions that flawlessly integrate with diverse cloud providers
• Assume a pivotal role for the compute and hardware architecture domain, driving expertise and excellence
• Showcase a multidisciplinary understanding of Ethernet, InfiniBand, data center LAN (local area networking), WAN (wide area networking), WiFi, and SD (software-defined) networks
• Conduct TCO analysis, optimizing datacenter efficiency for cost-effectiveness

Senior Solutions Architect – Enterprise AI

Product Marketing

• Develop a worldwide channel GTM collaboration with strategic goals aligning with NVIDIA's enterprise strategy
• Work closely with NVIDIA's extended team and global HP, Inc organization
• Gain strategic alignment with channel leadership to influence plans and investments
• Develop a trusted advisor connection with HP, Inc's partner leadership
• Build a closed-loop system identifying progress against key outcomes
• Identify leaders from HP, Inc's partner organizations to drive pipeline and incremental sales
• Collaborate with cross-functional teams to produce executable plans and improve NVIDIA's value through incremental pipeline and revenue

Global Channel GTM Lead

• Design and implement enhancements to the CUDA C++ compiler driver and front-end, ensuring tight integration with industry-leading third-party compiler software
• Contribute to the CUDA C++ programming language specification and its evolution factoring in support for new GPU hardware capabilities
• Drive language conformance, front-end robustness, and compile-time improvements that benefit a wide range of users
• Provide engineering support for the compiler and deliver targeted changes for production customer releases
• Establish strong customer/partner relationships and trust through excellent execution and high-quality software development