NVIDIA

Security Engineer

• Craft and build autonomous and semi-autonomous LLM red team agents for reconnaissance, hypothesis-driven exploitation, and evidence capture against NVIDIA-owned targets
• Extend agent harnesses with multi-phase orchestration, parallel specialist subagents, judge/verifier stages, and out-of-band callback infrastructure
• Encode offensive security tradecraft into prompts, tools, and playbooks across web application, cloud, Kubernetes, and related attack paths
• Build exploit-confirmation harnesses to validate findings and reduce false positives
• Engineer safety controls including sandboxing, scope enforcement, tool allowlists/blocklists, credential isolation, and prompt-injection defenses
• Evaluate and benchmark frontier models for offensive tasks
• Partner with the human red team to convert engagements into repeatable agent capabilities
• Mentor engineers and set the technical bar for agentic offensive tooling

Senior Offensive Security Engineer, Vulnerability Operations

Full-stack Engineer

• Support day-to-day operations of the vulnerability management hub and two AI scanning engines
• Monitor scan fleets and worker queues
• Investigate stuck or failed scans and ship fixes
• Build and maintain Python/FastAPI backend services, React/TypeScript frontends, and Postgres data models
• Triage platform feedback from security engineers and repository owners
• Reproduce bugs, run SQL data reconciliations, and close the loop with users
• Operate the GitOps deployment pipeline using Kubernetes/OpenShift, ArgoCD, and Vault
• Ship releases to staging and production
• Rotate secrets and validate rollouts
• Improve scan coverage and data quality across GitLab, GitHub, Gerrit, and Perforce
• Improve ownership attribution, finding lifecycle, and deduplication logic
• Work alongside senior engineers on LLM agent harnesses for scanning and verification

Security Software Engineer, Vulnerability Operations

Production Engineer

• Define the long-range technical strategy for operating DGX Cloud clusters consistently across on-prem, hyperscalers, and NeoCloud environments
• Define the architectural vision and core operational guidelines for cluster lifecycle, runtime delivery, restoration, release readiness, and steady-state operability throughout DGX Cloud resources
• Guide the roadmap and execution of critical cross-organizational investments that improve production readiness, operational safety, performance, and cross-team coordination
• Make and guide high-impact technical decisions resolving how platform, hardware, provider, and service teams coordinate to operate DGX Cloud resources in production
• Develop robust workflows, interfaces, and engineering collaboration across Kubernetes production service, provider and hardware readiness, on-prem and bare-metal infrastructure operations, and service-layer reliability domains
• Act as a senior technical leader in the Production Engineering group
• Build the architectural direction for cluster operations in DGX Cloud
• Set operating standards and direct the evolution of the production model
• Drive delivery of cross-organizational capabilities ensuring DGX Cloud resources remain usable, maintainable, and continuously improved at scale
• Lead by influence across several teams and critical production results

Distinguished Engineer, Production Engineering, Data Center Automation

• Explore innovative ways to make GPU accelerated applications easier to develop, deploy, and monitor
• Enable GPUs as first-class resources in container orchestrators such as Kubernetes
• Work with the core group on Cloud Native technologies enabling NVIDIA accelerators in the Kubernetes environment
• Collaborate with engineering teams across NVIDIA to ensure software integrates seamlessly across the stack
• Multitask across different tracks to address evolving priorities

Senior Systems Software Engineer, Containers, Kubernetes

Engineering Manager

• Partner with multiple internal teams to gather requirements, provide feedback to engineering teams, and develop solutions to aid in their success
• Develop, guide, and supervise a team of engineers
• Educate partner teams on standard methodologies for cloud-based solutions
• Gain a deep understanding of how NVIDIA develops across multiple verticals, including autonomous vehicles, chip manufacturing, hardware design, and AI
• Present to internal partners and NVIDIA leadership on roadmaps, vision, and demos
• Participate in open-source communities of software NVIDIA uses and builds
• Manage a software engineering team for the NVIDIA DGX Cloud Kubernetes organization, supporting internal and external clients
• Help build and operate a GPU cloud platform using NVIDIA technologies and Kubernetes

Manager, Software Engineering – DGX Cloud

Architect

• Define and guide protocol and architecture direction for RDMA and advanced transport technologies across next-generation networking platforms
• Lead technical work spanning RDMA semantics, congestion management, multipath transport, reliability, ordering, virtualization, and software-visible interfaces
• Develop NVIDIA’s viewpoints and contributions across UEC, IBTA, IEEE, OCP, and other standards organizations
• Collaborate with architecture, hardware, firmware, software, validation, and product teams to turn protocol concepts into practical features and roadmap investments
• Evaluate standards proposals, protocol extensions, and ecosystem requirements; identify tradeoffs and drive technical recommendations
• Engage internal and external contributors to advance interoperability, performance portability, and RDMA ecosystem adoption
• Use analysis, modeling, experimentation, traces, and system-level reasoning to validate architectural decisions and identify bottlenecks or protocol gaps
• Represent technical direction to senior leaders, engineering teams, and external collaborators

Senior RDMA, Networking Architect

Systems Engineer

• Design and develop advanced features for world-class embedded processing
• Write clean, efficient, and maintainable code using advanced AI-based tools
• Collaborate with team members, SW R&D, SW Architects, compiler team, and FW
• Contribute to the development of advanced networking and embedded processing software
• Develop next-generation embedded processor software

Senior Software Engineer – Embedded CPU SDK

DevOps Engineer

• Join the NVIDIA AIR team building a SaaS/IaaS platform for digital twins of AI data centers
• Handle DevOps, infrastructure, and Site Reliability Engineering (SRE) requirements for NVIDIA AIR
• Automate repetitive workflows to improve efficiency
• Work on a microservices-based architecture
• Deploy and troubleshoot non-disruptive cloud operations with emphasis on secure production infrastructure
• Continuously evaluate existing systems and drive improvements
• Manage deployment and upgrades for operating systems, Kubernetes clusters, and other orchestration tools
• Provide day-to-day support for engineering activities using CI/CD tools such as Git and Jenkins
• Manage multiple work tracks and evolving priorities
• Build or maintain metrics collection and alerting infrastructure

Senior DevOps Engineer

• Lead research, design, development, and implementation of attestation and confidential compute solutions for network devices
• Apply innovative security primitives using attestation capabilities
• Collaborate across external and internal teams and with customers
• Define next-generation architecture following standards bodies
• Develop proof-of-concepts for attestation and confidential compute

Senior Security Architect

• Explore networking features and protocols, hardware/software capabilities, open-source software and drivers for Ethernet Switch ASICs and Networking platforms
• Be familiar with Ethernet Switch ASIC hardware and software stacks, as well as Ethernet Switch platform design and characteristics
• Define and implement Proof-of-Concepts (PoCs) using network/system simulations
• Write technical requirements for embedded software meeting AI/ML workloads' needs and high-performance network operations
• Collaborate closely with R&D and Validation teams, providing technical guidelines and close support

Networking Software Architect

Engineer

• Design, implement, and maintain custom Rust-to-GPU compiler backends, including rustc codegen backends and proc-macros
• Build Rust-native intermediate representation frameworks to compile standard Rust to CUDA PTX and machine code
• Lower Rust AST and MIR into MLIR, PTX, LLVM, and other IRs, including GPU-specific optimizations
• Build tooling for ahead-of-time, just-in-time, and link-time optimization workflows targeting NVIDIA GPUs, host platforms, and feature sets
• Architect compiler-enforced safety models extending Rust ownership, borrowing, and lifetime disciplines across the GPU launch boundary
• Implement type-safe Rust abstractions for shared memory, barriers, scoped atomics, Tensor Memory Accelerator, and warp/cluster-level operations
• Build composable building blocks for future accelerated-computing applications

Senior Compiler Engineer

• Work on CUDA Tile, a tile-based programming model for NVIDIA GPUs
• Design and implement compiler transformations
• Develop MLIR-based dialects and lowering passes
• Optimize tile-based kernel performance across multiple generations of NVIDIA GPU architectures
• Define public APIs
• Craft and implement compiler and optimization techniques
• Perform performance optimization and general software engineering work
• Collaborate with a top-notch team to impact the deep learning community

Senior DL Compiler Engineer – CUDA Tile

• Maintain, tune, and secure mission-critical Linux operating systems across production and non-production environments
• Lead infrastructure configuration management and continuous deployment workflows using Infrastructure as Code, including Ansible and Terraform
• Write high-quality, maintainable Python, Bash, or Go code to automate repetitive operational tasks and integrate infrastructure telemetry
• Manage Linux Naming Services, including LDAP/SSSD, and networked storage environments, including NFS and Autofs
• Design, deploy, and maintain enterprise-grade Linux environments across RPM-based distributions
• Drive automation initiatives and build robust tooling for highly available and scalable EDA compute-farm infrastructure

Senior Linux Systems Engineer – EDA Infrastructure

Solutions Engineer

• Collaborate with NVIDIA Cloud Partners to create, implement, and deliver NVIDIA's innovative hardware and software solutions
• Partner with SAs, Account Managers, Engineering, Product, and business leaders to align on strategies, assess technical needs, and secure business opportunities for NVIDIA
• Become the primary technical driver for customers during the design, development, construction, integration, and production of GPU Cloud infrastructure and applications throughout the entire customer lifecycle
• Conduct regular technical customer meetings for project/product details, feature discussions, introductions to new technologies, and debugging sessions
• Work closely with customers to build and adopt NVIDIA solutions, including PoCs, to address critical business needs covering infrastructure, libraries, and applications
• Prepare and deliver technical content to customers, including presentations, workshops, reference architectures, tutorials, and publications
• Drive outtake and consumption by integrating libraries, frameworks, models, and software applications
• Deliver GenAI, AI, and ML hardware/software to production with consequential customers and partners
• Own end-to-end technology solution integration with strategic customers and offer product strategy recommendations based on feedback

Principal Solutions Architect

• Define and drive SoC-level RAS hardware architecture across CPUs, interconnects, memory systems, IOs, safety islands, firmware interfaces, and platform-level components
• Own RAS features from concept through architecture specification, micro-architecture alignment, RTL implementation support, design verification, silicon validation, debug, and production readiness
• Develop architectural requirements for fault detection, correction, containment, isolation, telemetry, error reporting, recovery, graceful degradation, serviceability, and diagnostic observability
• Collaborate with design, verification, validation, firmware, software, and platform teams
• Analyze interactions between RAS mechanisms and performance, power, reset flows, clocks, memory hierarchy, interconnect behavior, firmware-visible controls, and platform software
• Create hardware specifications, architectural requirements, error-handling flows, design guidance, test plans, and architectural models using SystemC, C/C++, Python, or other relevant modeling environments
• Plan and review verification and validation strategies, including error injection, recovery validation, coverage analysis, resiliency modeling, and cross-functional architecture reviews
• Assist in failure analysis and silicon debug for lab, post-silicon, production, and field findings
• Develop diagnostic screens and localization methods for latent, intermittent, and environment-sensitive failures
• Apply RAS architecture principles to high-reliability, space-aware, and radiation-aware deployment environments
• Follow industry standards and best practices related to RAS, functional safety, semiconductor reliability, debuggability, verification, validation, and silicon testing
• Patent novel hardware architecture techniques improving system resiliency, observability, serviceability, and recovery