NVIDIA

Security Engineer

• Develop and answer open-ended AI security research questions that helps NVIDIA understand, measure, and reduce risk in frontier models, agentic systems, AI platforms, and AI-enabled products.
• Develop practical methods, prototypes, evaluations, or tools that reveal how AI systems can fail under adversarial conditions and how those risks can be mitigated.
• Explore a range of AI security problems, such as LLM and agent security, adversarial testing, model evaluation, cyber-defense automation, vulnerability discovery, secure deployment, or autonomous response.
• Translate research into usable outcomes for engineering and security teams, including proof-of-concept demonstrations, benchmarks, technical guidance, mitigations, and secure-by-design recommendations.
• Collaborate across offensive security, product security, AI research, platform, cloud, and infrastructure teams to connect research insights with NVIDIA's highest-impact security priorities.
• Help shape NVIDIA's AI-security research strategy by mentoring others, identifying emerging risks, and building repeatable practices for evaluating and defending AI systems.

Senior AI Security Researcher

Engineer

• Investigating the latest and future NVIDIA GPU hardware architecture and programming models.
• Working on the frontier of AI by understanding advanced algorithms (like attention sinks and MoEs) and numerics (like block-scaled floating point) to identify new opportunities for optimization.
• Designing and implementing compiler technology using MLIR to optimize high-level kernel descriptions (written in Triton's Python DSL), with a focus on generating efficient, low-level GPU code.
• Engaging in a dynamic, iterative process of optimization—sometimes starting with the kernel, sometimes with the compiler—to find the most efficient path to peak performance.
• Collaborating with teams across NVIDIA, including hardware architects and the CUDA compiler team, to influence future products and ensure we are always operating at maximum efficiency.

Senior Performance Compiler Engineer – Triton

Full-stack Engineer

• Own and evolve OpenShell’s CI/CD system across GitHub Actions, self-hosted Linux amd64/arm64 runners, GPU runners, macOS runners, reusable workflows, gated e2e jobs, release canaries, and developer-facing branch checks.
• Build and harden multi-architecture release pipelines for GHCR images, Helm OCI charts, Linux and macOS CLI binaries, gateway and sandbox binaries, Python wheels, Debian packages, RPM packages, Homebrew formula generation, and install scripts.
• Improve release reliability for both rolling dev builds and tagged public releases, including version derivation, automatic tagging, checksums, artifact pruning, provenance, artifact attestations, and downstream package publishing.
• Drive reproducible and performant builds using mise, uv, Cargo, maturin, BuildKit, Docker/Podman, sccache, native amd64/arm64 runners, Zig, osxcross, protobuf codegen, and pinned toolchains.
• Own the quality gates that decide whether code is safe to merge or ship, including Rust/Python checks, license headers, markdown/docs validation, e2e label gates, Docker/Podman e2e, Kubernetes/Helm e2e, GPU e2e, and release canary coverage.
• Debug difficult build and release failures across containers, registries, runners, package managers, cross-compilation toolchains, kernel/VM runtime artifacts, and CI cache behavior.
• Partner with platform engineers to make OpenShell easier to install and operate across Linux, macOS, Kubernetes, Docker, Podman, GPU environments, and experimental VM/libkrun-based runtimes.
• Continuously improve CI observability, failure diagnostics, workflow runtime, cache hit rates, artifact traceability, and the developer experience for contributors and maintainers.

Software Engineer, Build and Release

Cloud Engineer

• Work closely with Engineering, Infrastructure, and Software teams
• Lead programs focused on telemetry and data center fleet health & management
• Establish feedback loop for telemetry requirements across departments
• Drive end-to-end telemetry lifecycle for NVIDIA Cloud Providers
• Collaborate across technical domains to establish standard telemetry operations
• Provide leadership and mentorship to the DGXC TPM organization
• Improve day-to-day efficiency by building AI tools

Senior Technical Program Manager, Cloud Infrastructure, Observability, and Systems Monitoring

AI Research Scientist

• Help trailblaze company efforts in applying AI within conventional compilation pipelines.
• Design and implement AI-based technology addressing core problems of low-level GPU programming.
• Build training pipelines for supervised fine-tuning and reinforcement learning (RL/RLHF-style or policy optimization variants).
• Define model inputs/outputs over compiler low level compiler representations.
• Develop evaluation frameworks to measure code quality, runtime, compile-time overhead, and correctness.
• Intelligent (domain task based) prompt engineering.
• Collaborate with compiler engineers to integrate learned policies into production toolchains.
• Prototype and iterate on model architectures, prompts, and fine-tuning strategies for scheduling and allocation tasks.
• Create datasets from compiler traces, optimization passes, and target-specific performance signals.
• Apply RL techniques to optimize for downstream objectives (performance, spill reduction, instruction-level parallelism, etc.) and run rigorous experiments, ablations, and benchmarking across workloads and hardware targets.

AI Research Engineer – Applied Scientist, Compilers

Program Manager

• Hands on leadership of a team coordinating projects, partnering with tech leads to understand and solve sophisticated planning and controls challenges, including AI based planners.
• Drive and be responsible for delivery of your technology portfolios, in partnership with your engineering leader counterparts.
• Coordinate and collaborate with teams and internal customers to deliver those roadmaps against extremely ambitious timelines and goals: build processes and frameworks to amplify team performance, communicate and resolve bottlenecks, address issues, and measure outcomes.
• Clearly represent the group and technical product status and program execution to senior executives and OEM partners.

Senior Manager, Technical Product and Program Management, Planning and Controls – Autonomous Vehicles

Systems Engineer

• Develop, evaluate, and build architectures for SAE Level 2 to Level 4 driver assistance and self-driving vehicle technologies
• Characterize autonomous vehicle interactions relative to vehicle requirements to drive data collection for system training and verification
• Drive autonomous vehicle system verification activities, including architecture and design verification, test strategy development working with the System Integration & Verification Test Leads, and reviewing and prioritizing test results
• Lead and communicate data analysis, working with product and engineering teams to drive design decisions relative to requirements and system behavioral needs
• Develop methods to analyze and compare the impact of ODDs on relevant performance metrics, translating data and analysis into impactful recommendations

Senior Software Systems Engineer – Autonomous Vehicles

• Define and guide the complete security architecture for global cloud and datacenter infrastructure
• Ensure strong protection of critically important AI workloads against advanced adversaries
• Architect robust security frameworks for cloud infrastructure across public (AWS, Azure, GCP, Oracle) and private GPU/AI environments
• Implement network segmentation strategies using SDN techniques for multi-tenant isolation
• Facilitate adoption of Confidential Computing and hardware-rooted trust models for high-sensitivity AI workloads
• Craft layered encryption schemes to mitigate risks and adhere to Zero Trust principles
• Govern workload identity frameworks to eliminate static credentials and implement identity-based access controls
• Direct security architecture in container and Kubernetes environments to maintain secure, scalable multi-tenant clusters
• Provide technical leadership and mentoring to a team of security architects and engineers

Cloud Security Architecture Lead

• Help develop software infrastructure to characterize and analyze a broad range Deep Learning applications
• Evolve cost-efficient datacenter architectures tailored to meet the needs of Large Language Models (LLMs).
• Work with experts to help develop analysis and profiling tools in Python, bash and C++ to measure key performance metrics of DL workloads running on Nvidia systems.
• Analyze system and software characteristics of DL applications.
• Develop analysis tools and methodologies to measure key performance metrics and to estimate potential for efficiency improvement.

Senior Deep Learning Systems Engineer, Datacenters

Architect

• Develop full-system functional models capable of running complex multi-threaded heterogeneous (CPU/GPU) workloads – with special focus on the CPU subsystem.
• Integrate functional models from various frameworks with RTL simulators and emulators, hardware (HW-in-the-loop), and detailed performance models.
• Bring up system and application software in simulation and emulation – including firmware, Linux, drivers, benchmarks, and CPU/GPU workloads such as deep-learning (DL) and high-performance computing (HPC) workloads.
• Port/extend/develop system software (firmware, OS, and drivers) to meet workload simulation needs.
• Support CPU architects and performance engineers in their use of functional models, performance models, and emulation to drive next-generation CPU architectures.

Senior System Simulation Architect

Developer Relations

• Act as a trusted technical advisor for research labs, identifying and accelerating high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects.
• Map and continuously assess the research ecosystem to identify institutional growth opportunities and inform long-term technology strategies
• Stay current on research papers across affiliated domains to anticipate emerging trends and provide technical direction on future collaboration areas.
• Collaborate cross-functionally with Research Account Managers, Solution Architects, and Business Development teams to drive researcher enablement
• Forge closer ties with lab personnel, understanding organizational dynamics and the full scope of research being conducted.
• Attend domain scientific conferences and facilitate NVIDIA participation alongside other subject matter experts.
• Represent the needs of researchers internally by translating academic feedback into actionable insights that shape product development, education programs, and platform roadmaps.

Developer Relations Manager – Higher Education and Research, AI Agents

DevOps Engineer

• Design, implement and support operational and reliability aspects of large scale Observability & Telemetry collection platform with a focus on performance at scale, real time monitoring, logging and alerting
• Engage in and improve the whole lifecycle of services—from inception and design through deployment, operation and refinement
• Support services before they go live 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 Site Reliability Engineer – Observability, Telemetry Platform

Data Engineer

• Architect event-driven pipelines (Kafka) and develop new data models to ensure transactional integrity for commercial events
• Automate scalable ETL processes and refactor next-generation data architectures for quality and security
• Collaborate across teams to codify business processes into self-measuring systems, debugging complex challenges

Senior Systems Data Engineer, Financial Transactions, Automation

Account Manager

• Grow NVIDIA's education technology (EdTech) ecosystem
• Build trusted relationships with executive leaders at partner companies
• Coordinate across internal teams to deliver full value of NVIDIA
• Work with Developer Relations Leaders to accelerate partner workloads
• Co-create joint go-to-market plans with IBD Lead

Strategic ISV Account Manager – Education Technology

Solutions Engineer

• Helping our partners succeed in their adoption of end-to-end solutions using NVIDIA's graphics, compute, edge, and AI systems.
• Deeply understanding customer workflows and guiding them to the best NVIDIA hardware and software to solve their challenges.
• Delivering demos, assisting with proof-of-concepts, or writing papers and developer blogs.
• Collaborating with executives and engineering to tackle sophisticated problems and help bring NVIDIA's premiere technologies to life.