NVIDIA

Artificial Intelligence

• Stay at the forefront of technology trends in AI Infrastructure & Agentic AI, proposing new product concepts
• Define detailed product architectures, including performance, scalability, interoperability, and datacenter deployment requirements
• Lead prototyping and testing processes to validate product design and functionality, focusing on scale, diagnosing bottlenecks, and driving performance innovations
• Iterate and refine designs based on feedback, testing results, and evolving requirements
• Collaborate with internal hardware, software, security, and systems teams to integrate networking, compute, and storage ecosystems
• Work with partners and customers to conceptualize solutions, build proof-of-concepts (POCs) and minimum viable products (MVPs)
• Provide comprehensive product design documentation, including technical specifications and user guides
• Develop whitepapers, blogs, and curriculum for customers, partners, and users

Senior Product Architect, K8s-Based AI Infrastructure

Developer Relations

• Develop and maintain deep technical expertise in Splunk, AppDynamics, ThousandEyes, and AI Defense.
• Serve as a trusted technical advisor, problem solver, and developer advocate for observability, security operations, and AI platforms.
• Partner with cross-functional teams to drive adoption of NVIDIA libraries.
• Demonstrate and integrate NVIDIA APIs, SDKs, data integrations, apps, and reference architectures into partner products, platforms, and pipelines.
• Guide partners and developers through onboarding and integration with sample code, guides, and demo pipelines.
• Map and monitor the developer ecosystem to identify growth opportunities and inform roadmaps and partner adoption strategies.
• Engage partner technical leaders to drive best-practice integrations, resolve technical challenges, track adoption, and relay field insights to NVIDIA product teams.
• Grow and activate developer communities through conferences, meetups, hackathons, technical talks, and hands-on workshops.
• Drive API, SDK, and integration adoption across Splunk and the broader software ecosystem.
• Translate developer sentiment and ecosystem trends into actionable intelligence for product direction and platform strategy.

Senior Developer Relations Manager – Security and AI Software

Engineer

• Prototype and integrate novel GPU-accelerated approaches for dataframe analytics, compression, transactional databases, and vector databases
• Analyze and optimize complex data-intensive workloads across heterogeneous GPU/CPU architectures
• Collaborate with research, hardware, system software, libraries, and tools teams to influence next-generation hardware, software, and programming models
• Work with NVIDIA customers and cloud service providers to integrate solutions at Speed-Of-Light
• Influence open standards in data analytics and compression

Senior DevTech Engineer, Compression and Data Processing

• Develop compiler optimization algorithms for deep learning workloads
• Optimize inference and training performance for the JAX framework and OpenXLA compiler on NVIDIA GPUs at scale
• Collaborate with deep learning framework partners and hardware architecture teams
• Craft and implement compiler optimization techniques for deep learning network graphs
• Design graph partitioning and tensor sharding techniques for distributed training and inference
• Perform performance tuning and analysis
• Implement code generation for NVIDIA GPU backends using MLIR, LLVM, and OpenAI Triton
• Design user-facing features in JAX and related libraries
• Perform general software engineering work
• Work with GPU hardware engineering teams to design AI compiler software features for next-generation GPUs
• Define project goals and scope and lead development efforts
• Apply software engineering and testing practices
• Mentor junior engineers and interns

Senior Deep Learning Compiler Engineer – XLA

Solutions Engineer

• Solve hard Day 2 operations problems at scale alongside partner engineers.
• Find causes, prototype approaches, validate them under representative load, and leave partners with operable practices.
• Prepare partners for new NVIDIA platforms, capacity, services, and use cases.
• Drive adoption in live environments without degrading service.
• Improve reliability, performance, utilization, recovery time, and cost per token.
• Identify and help close maturity gaps across people, process, tooling, telemetry, security, and incident response.
• Convert validated work into operating procedures, reference architectures, assessments, automation, and agentic workflows.
• Identify cross-partner patterns and provide field evidence to account teams, support, product, and engineering.
• Improve NVIDIA Cloud Partner Day 2 operations and ecosystem capability.

Senior Solutions Architect, Cloud Partner Operations

Research Scientist

• Invent and reformulate numerical algorithms co-designed for modern NVIDIA GPU architectures, including implicit and explicit engineering simulation
• Develop linear and nonlinear solver approaches, including Newton-Krylov, multigrid and AMG, domain decomposition, matrix-free, mixed precision, sparse iterative and direct methods, and preconditioning strategies
• Investigate GPU-native alternatives to CPU-oriented numerical methods, including synchronization-avoiding Krylov, GPU-native multigrid and domain decomposition, matrix-free implicit, mixed-precision, and sparse direct/iterative hybrid methods
• Evaluate algorithms on workloads in mechanics, contact, thermal-fluid systems, electromagnetics, semiconductor process and device simulation, EDA, multiphysics, and related CAE domains
• Collaborate with CUDA-X, Warp, solver engineering, NVIDIA Research, universities, and industry partners to move research prototypes into NVIDIA software capabilities
• Help shape the applied research roadmap for GPU-native numerical methods and their evolution toward AI-native computational engineering

Senior Applied Research Scientist – GPU Native Numerical Algorithms

Mechanical Engineer

• Verify chip blocks/entities according to specifications
• Develop integrations and full-chip models
• Interact with organization-wide groups
• Contribute to verification of high-speed communication and networking silicon devices

Chip Design Engineer

Full-stack Engineer

• Create and implement training infrastructure spanning pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL) post-training for Physical AI world foundation models
• Develop and improve pre-training and SFT pipelines, including large-scale data loading, distributed training, and checkpointing
• Develop and improve the inference and evaluation stack, including inference engines, inference/generation pipelines, RL rollout support, and evaluation pipelines
• Use continuous batching and KV-cache management to achieve high throughput and low latency
• Build and improve interaction and data flow among RL system roles: policy, rollout, reward, and simulation
• Integrate and orchestrate simulation and robotics environments as RL environments, driving the simulation–rollout–training loop at scale
• Build and refine the distributed training backend, including sharding/parallelism, mixed precision, activation checkpointing, and multi-GPU memory/throughput optimization
• Improve efficiency, scalability, and resiliency of training and RL workloads through fault tolerance, fast/elastic restart, and throughput optimization under preemption and hardware failure
• Define actionable reliability and efficiency metrics
• Root-cause, triage, and resolve failures from the application level through framework, GPU, network, and hardware levels

AI Infrastructure Software Engineer

DevOps Engineer

• Build tools to improve SRE observability
• Participate in the Kubernetes migration journey with VMI setup and problem solving
• Rapidly debug and triage incidents and user-reported issues
• Automate, script, and develop tooling for new and existing scripts to achieve 100% automation of daily tasks
• Support services before launch through system design consulting, software platform and framework development, capacity management, and launch reviews
• Participate in an on-call rotation supporting production systems
• Drive tools and service development to maintain and improve service SLOs
• Partner with Service Owners to drive service reliability
• Lead production improvements, including change management, post-mortem reviews, workflow processes, and software automation

Senior Site Reliability Engineer

• Perform physical design of CPU on-chip interconnect networks and last-level caches
• Execute implementation, synthesis, timing closure, and timing analysis
• Collaborate with the logic design team on micro-architecture definition and feasibility
• Achieve timing, area, performance, and power goals
• Evaluate physical design for next-generation NVIDIA coherent interconnects and system-level caches
• Work with the global CPU team on high-performance semiconductor designs

Senior ASIC Physical Design Engineer, Cache Coherent Interconnects

• Architect and develop boot firmware for flagship CPUs used in automotive, datacenter, and other critical applications
• Build robust features for core boot firmware following protective and security guidelines
• Define, decompose, and verify functional and safety requirements and architecture
• Perform failure analysis (FMEA/DFA) on bootloader functionality and architecture
• Collaborate with internal and external team members
• Validate and verify features on pre-silicon simulation platforms before tapeout
• Perform silicon verification and feature development
• Provide post-silicon support and productization

Senior System Software Engineer – CPU SoC Boot Firmware

• Help partners build scalable, observable, GPU-accelerated Physical AI pipelines through agentic workflows, cloud-native technologies, and NVIDIA frameworks such as OSMO.
• Support development of Physical AI data factories for data ingestion, preprocessing, annotation, filtering, synthetic data generation, training, simulation, and evaluation.
• Develop a deep understanding of robotics workload scaling and translate customer requirements into optimized cloud-native architectures.
• Improve scheduling, cost, storage access, networking, and GPU utilization across hybrid infrastructure.
• Accelerate distributed inference using NVIDIA technologies such as NIM, TensorRT-LLM, vLLM, and SGLang.
• Collaborate with business, engineering, and product teams.
• Provide technical guidance and mentorship to customers implementing Physical AI at scale.

Senior Solutions Architect, Physical AI Cloud

Field Engineer

• Drive development and execution of functional, regression, and stress testing for networking and GPU technologies within OEM server environments
• Lead hands-on debugging, firmware validation, and isolation of complex system-level issues for new server generations and fast-release programs
• Integrate hardware and software components into an architecture that automates end-to-end testing scenarios for packaging and deployment
• Act as a technical lead in weekly issue scrubs to resolve manufacturing blockers, hardware diagnostics failures, and customer partner concerns
• Provide onsite technical support for hardware/software problems
• Maintain lab systems to ensure platform readiness
• Serve as the technical link connecting OEM Engineering with NVIDIA product units
• Validate and troubleshoot NVIDIA networking technologies, including NICs, DPUs, switches, cables, and DOCA software, for OEM server platforms

Networking Field Applications Engineer

• Spearhead the conception, development, and execution of data center networks for NVIDIA customers under the Plan, Design, & Implement framework
• Advance comprehensive technology solutions for strategic customers in web2.0, cloud, HPC AI, and enterprise datacenter domains
• Identify business technology improvement areas and provide recommendations to marketing and engineering teams
• Provide Ethernet and routing expertise during project delivery to design, architect, configure, and test networking solutions
• Work with multifunctional teams on server infrastructure builds, accelerated computing workloads, and GPU-enabled AI applications
• Craft and evaluate DevOps automation scripts, design architectures, and develop switch fabric configurations
• Implement network configuration and validation tasks for data centers
• Create Methods of Procedure and deployment documents
• Validate network architectures and configurations in the lab
• Create and customize network automation scripts for customer needs using AI tools
• Interact directly with customers to gather requirements and design optimal solutions
• Use software and AI tools to validate and monitor network performance
• Create project handover documents for customers
• Travel onsite to customers' data centers for initial configuration work, up to 20%

Solutions Architect, Ethernet Networking

• Build and deepen technical expertise in analytical data processing, including query execution and optimization, columnar and vectorized processing, and distributed execution.
• Serve as a technical advocate and trusted resource for developers building and operating analytical data systems.
• Drive adoption of NVIDIA technologies including Sirius, cuCascade, RAPIDS, cuDF, nvCOMP, and CUDA-X Data Processing.
• Demonstrate and integrate NVIDIA’s data processing stack into query engines and OLAP databases across cloud, hybrid, and on-prem deployments.
• Provide reference implementations, integration guides, onboarding, and hands-on engineering support.
• Track analytical data processing engines, execution models, storage formats, and competing acceleration approaches.
• Share ecosystem insights with NVIDIA engineering, product, marketing, and worldwide field teams.
• Collaborate with engine architects and NVIDIA engineering to resolve integration problems and establish best practices.
• Define the integration surface between NVIDIA GPU data-processing libraries and third-party query engines.
• Specify required APIs and carry successful integration patterns forward.
• Design and run TPC-H, TPC-DS, and ClickBench measurements against CPU baselines.
• Publish benchmark results, assess acceleration effectiveness, and identify required changes.
• Own the acceleration roadmap jointly with projects and partners.
• Earn ecosystem influence through upstream contributions, public design reviews, and joint architecture and roadmap planning.