NVIDIA

Cloud Engineer

• Outline the security architecture strategy for cloud authentication, authorization, workload identity, and agent identity across NVIDIA cloud platforms, AI-enabled systems, enterprise connectors, services, and automation.
• Outline processes for establishing, linking, authorizing, delegating, auditing, and retiring human, workload, service, and autonomous agent identities.
• Develop authorization and delegation frameworks for AI agents and enterprise connectors, encompassing consent, token exchange, prioritized authority, sensitive-action approval, revocation, and protections against confused-deputy behavior.
• Lead architecture reviews and threat modeling for high-risk identity and access flows.
• Establish identity lifecycle, telemetry, and emergency-disablement patterns for token issuance, policy decisions, privilege elevation, tool invocation, data access, credential rotation, grant revocation, and compromised or untrusted identities.
• Convert emerging AI security risks into authentication, authorization, audit, and execution-boundary requirements.
• Partner with identity, cloud, platform, application, AI security, governance, detection, and incident response teams to align architecture decisions with risk strategy and operational reality.
• Build reusable architecture patterns, decision records, exception criteria, and implementation mentorship.

Senior Security Architect – Cloud Authentication, Authorization

Infrastructure Engineer

• Ability to resolve sophisticated customer concerns and technical issues through meticulous research, reproductions, and solving problems for customers installing our products and supporting systems using Linux Operating Systems (multi-distro).
• Responding to customer product support inquiries via telephone, email or conference calls
• Resolving customer issues during installation, operation, maintenance or product application or interoperability with other vendors
• Participate in multi-functional team meetings and giving feedback to engineering and marketing regarding product requirements, customer experience, support tools, etc.
• As a technical resource develop, re-define and document standard methodologies to provide to internal teams(Support/R&D) for support process and improvements

Senior HPC Support Engineer – Ethernet, AI Infrastructure

Backend Engineer

• Scope and build tools, microservices, workflows, and distributed applications to accelerate data mining and AI training.
• Design and implement solutions for streaming, resilience, logging, security, authentication, workflow orchestration, and data management.
• Deploy AI models.
• Design and develop Retrieval-Augmented Generation (RAG) workflows enabling hybrid and agentic patterns.
• Analyze and operationalize complex distributed systems for speed-of-light performance.

Senior Data Backend Engineer

Machine Learning Engineer

• Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
• Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
• Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
• Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
• Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
• Partnering with leadership to ensure infrastructure scales with growing GPU capacity and dataset size while maintaining developer efficiency and stability.

Senior Deep Learning Engineer – Autonomous Vehicles

Solutions Engineer

• Deliver innovative and optimized AI agents using the latest techniques including Test Time Compute, Reinforcement Learning, inference optimization and model fine-tuning.
• Work with agentic frameworks to develop applications that retrieve and generate insights from enterprise data, including text, code, and images.
• Create high-impact solutions such as deep research assistants, multi-modal dialogue systems, and task-specific agents that support a wide range of enterprise workflows.
• Engage with engineering teams, stay ahead of the latest AI advancements and apply strong technical judgment to deliverables.
• Provide direct feedback from first-time implementations to improve software products and scale knowledge by educating vertical teams and building communities on NVIDIA AI software products.

Solutions Architect

Systems Engineer

• Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping.
• Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments.
• Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads.
• Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines.
• Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency and programmability.
• Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning.
• Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products.

Senior Software Engineer, CUDA Deep Learning Systems

Mechanical Engineer

• Physical design of blocks according to specifications under challenging constraints targeting for the best power, area, and performance
• Be exposed and work on a variety of challenging designs (including high cell count and HS blocks)
• Resolving complex timing and congestion problems
• Daily work involves all aspects of physical design chip development (RTL2GDS) - synthesis, power and clock distribution, place and route, timing closure, power and noise analysis, and physical verification
• Taking part inflows development

Physical Design Engineer

Architect

• Define the Network Adapter chip architecture end to end from the market requirements through design and all product life cycles (post/pre-silicon, on deployments).
• Work with related industry standards & customers on deploying your tech.
• Collaborate with teams across teams (physical design, logic design, system software, firmware, applications).
• Work on proof of concepts and advance development projects.
• Perform research and analysis for current and future architectures.
• Craft product design specifications.
• Serve as a focal point within the organization and distribute the knowledge that you acquire.

Chip Architect

• Architect, Design, plan, implement and Operate complex PaaS for the GPU cloud services.
• Help drive the underlying technology stack and implementation methodology.
• Work closely with partners, stakeholders, UX and UI designers and front-end engineers on new products or features/improvements of existing products.
• Obsessed with modules and/or product(s) consistency across the teams.
• Nuts for test-driven development practices, help drive test automation, and CI/CD.
• Customer focused - support, maintain, and document software functionality.

Senior Cloud Software Engineer

• Working with NVIDIA AI Native and Consumer Internet customers on large data center GPU server and networking system deployments as Solution Architect Engineer.
• Guide customer discussions on network design, compute/storage and support bring up of server/network/cluster deployments.
• You will need to visit customer data center during bring up phase.
• Demonstrate subject matter expertise in advanced GPU & network systems and be a trusted technical advisor to NVIDIA's strategic customers.
• Bring customer-specific requirements to product teams to guide product roadmap features.
• Identify new project opportunities for NVIDIA products and technology solutions in data center and artificial intelligence applications.
• Work closely with the GPU/Network Systems Engineering, Product management and Sales teams
• Work as customer trusted advisor conducting regular technical customer meetings for product roadmap, cluster issues debug, feature discussions and introduction to new technology solutions
• Build custom product demonstrations and POCs for solutions that address critical business needs of our customers
• Analyze and debug compute/network configuration, performance issues to deliver performant clusters

Senior Solutions Architect, AI Infrastructure

Developer Relations

• Build and deepen technical expertise in AI/ML workloads in payments, serving as a technical advocate and trusted resource for developers in payments, with multi-functional partners to drive adoption of NVIDIA technologies
• Demonstrate and integrate NVIDIA’s software stack (tools, SDKs, libraries, NIMs, and blueprints) into real-world partner or developer projects to accelerate adoption
• Support developers and partners through onboarding and integration by providing code samples, guides, demo pipelines, and hands-on assistance that enable payments-specific use cases
• Research and monitor developer ecosystem trends (both payments and AI/ML wise) to identify opportunities for growth, sharing insights with engineering, product, and marketing teams to improve adoption strategies
• Collaborate with partner technical leaders and internal engineering stakeholders to solve challenges, guide best-practice integrations, and feed technical takeaways back to NVIDIA teams
• Be the leading voice for helping payment firms to adopt NVIDIA technologies, encouraging co-innovation and the development of next-generation AI/ML led payments solutions for use cases such as fraud detection, personalization, agentic commerce etc.
• Present NVIDIA’s work in payments to technical audiences at leading events and industry conferences

Developer Relations Manager – Payments

• Document and deliver design guidelines that will help the ASIC team implement high performance, area and power efficient RTL to achieve design targets and specifications
• Develop agentic AI solutions to craft the micro-architecture, implement the RTL, and deliver a fully verified, synthesis/timing clean design
• Collaborate and coordinate with other designers as well as tool and flow owners to make sure design guidelines and agentic solutions meet their requirements
• Drive impact by designing and deploying LLM-powered solutions for engineering assistants and multi-turn, multi-modal dialogue systems
• Stay ahead by engaging with the latest advancements in machine learning and AI to create powerful solutions

Senior ASIC Design Engineer

• Define high-level SoC subsystem architecture for LPU products
• Convert LPU product requirements into architectural specifications for uncore, IO, memory, firmware-facing, boot, reset, safety, fault tolerance, diagnostic, and power regulation subsystems
• Collaborate intimately with IP teams to develop detailed build documents for PCIe/CXL, NVLink/NVLink-C2C, UCIe, AXI/CHI, NoC fabrics, memory controllers, coherency blocks, MMUs/IOMMUs, boot, reset, and associated SoC infrastructure
• Specify subsystem behavior encompassing enumeration, capability discovery, configuration flows, sequence control of memory operations, data consistency, address mapping, interrupt handling, virtualization, error handling, and firmware/software-visible controls
• Build or guide functional and architectural models from specifications, using C++, SystemC, Python, or similar languages
• Use models to validate architecture intent, subsystem behavior, configuration sequences, and IP interactions before RTL or silicon is available
• Drive tradeoffs across bandwidth, latency, power, area, timing, scalability, reliability, security, debuggability, and software usability
• Review IP specifications, subsystem architecture documents, model behavior, verification plans, and validation strategies
• Collaborate with architecture, IP, firmware, software, RTL, verification, platform, and post-silicon teams to bring architecture decisions to completion

Senior SoC Subsystem and I/O Architect

• Drive GPU firmware & software work streams with CSP engineering teams — ensuring they understand GPU firmware architecture (VBIOS, InfoROM, microcontroller firmware), update sequencing, recovery procedures, and GPU power management
• Gather and synthesize CSP feedback on GPU firmware/software — covering manageability, observability, security requirements (e.g., multi-tenancy isolation, secure boot, attestation), and performance — and champion those priorities into NVIDIA's GPU firmware/software feature roadmap and delivery plan
• Drive GPU firmware update orchestration for large-scale deployments — multi-GPU update sequencing, rollback strategy, failure handling, and validation across hundreds of GPUs per rack
• Serve as the technical focal point between NVIDIA and CSP firmware/software engineering — ensuring GPU behaviors (error recovery flows, thermal protection, power state transitions) are well-documented and accessible for customer integration
• Identify cross-CSP GPU SW/FW issue patterns — common update failures, recovery gaps, and configuration problems — and drive documentation, tooling, and test strategy improvements

Principal Software Engineer, GPU Firmware, GPU System Software

Full-stack Engineer

• Drive performance characterization work streams with engineering teams of key CSP/hyperscale customers — ensuring they understand platform performance expectations, profiling methodology, and tuning options for their specific workloads
• Gather and synthesize CSP performance feedback — identify gaps between expected and actual throughput, and champion optimization priorities back into NVIDIA's CUDA, NCCL, driver, and firmware teams
• Ensure key open-source performance and stress tools (e.g., STREAM, GPU Burn, GPU BLAST) are updated and validated for the latest NVIDIA rack-scale systems, GPU architectures, and CPU platforms — so customers and internal teams have reliable baseline measurements from day one
• Work closely with CSPs to ensure their own performance and validation tooling reflects the latest GPU capabilities, memory hierarchy changes, and platform-specific tuning parameters
• Conduct cross-CSP performance comparison and pattern analysis — identify configuration, software, or workload differences that explain performance gaps between deployments
• Collaborate with CSPs to ensure performance-related integration work (profiling infrastructure, benchmark harnesses, config validation) is ready ahead of deployment milestones
• Define test strategies and tooling requirements for performance validation — both for NVIDIA internal certification and customer acceptance