NVIDIA

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

Full-stack 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.

Senior Software Engineer – CUTLASS Performance

• 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

• 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

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