Top Artificial Intelligence Jobs

Conduct original generative AI research across image, video, 3D, and audio generation. Build and train large-scale models, create research prototypes using extensive datasets and compute, collaborate with internal and external researchers, and transfer successful technologies to product teams.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

Conduct foundational research in generative AI for graphics and gaming, including neural rendering, real-time image/video/3D generation, world models, and AI-driven characters. Train large models on multi-GPU infrastructure, build large-scale datasets and data pipelines, prototype in game engines, and collaborate with product, hardware, driver, and platform teams to bring research into user-facing products.

Owns product strategy, roadmaps, and go-to-market plans for NVIDIA AI post-training and reinforcement learning frameworks, libraries, and SDKs. The role partners with researchers, production model builders, customers, leadership, and marketing to optimize large-scale performance, resilience, and user experience on NVIDIA GPUs. Responsibilities include creating and scaling software products, guiding open-source ecosystem efforts, and advancing research-to-production workflows.

Conduct fundamental research on novel large-scale generative AI methods, particularly for biomolecular and scientific applications. Design and implement models, publish original research, collaborate with research and product teams, present at conferences, and transfer technology to products. The role requires strong mathematical foundations, generative AI expertise, scientific data modeling experience, programming skills, deep learning framework knowledge, and a strong research record.

Lead the hands-on architecture and development of NVIDIA’s enterprise agentic AI platform. Build production systems in Python or Go, including multi-agent orchestration, RAG, memory, Kubernetes deployment, GPU inference, evaluation pipelines, observability, security, sandboxing, and policy enforcement. Rapidly prototype emerging approaches and harden successful solutions into reusable APIs, SDKs, services, and developer templates while influencing cross-functional engineering and business teams.

Lead NVIDIA’s agentic developer platform strategy and execution. Build scalable platform capabilities from emerging agent patterns, including RAG, vector databases, evaluation systems, governance, safety tooling, and GPU-optimized workflows. Integrate the AI Data Platform with NVIDIA’s on-prem AI Factory, improve distributed inference performance, and establish controls for data lineage, compliance, and Responsible AI. Grow a high-performing engineering team and drive adoption of reusable APIs, SDKs, components, and reference implementations across the company.

Designs and scales observability and telemetry platforms for distributed AI and HPC environments. Builds high-performance backend services, OpenTelemetry components, metrics pipelines, and real-time or batch data systems. Improves reliability, performance, capacity, and cost efficiency while developing monitoring, alerting, and service reliability frameworks. Requires production distributed-systems expertise, programming skills, time-series data experience, Kubernetes knowledge, and operational debugging capabilities.

Develop compiler optimization algorithms for deep learning networks and AI inference platforms. Collaborate with software framework and GPU architecture teams to improve performance, build times, memory usage, APIs, and compiler techniques for current and future NVIDIA GPUs. Analyze performance, implement and test compiler technologies, support CPU/GPU workloads, and independently lead development efforts.

Conduct fundamental research on novel large-scale generative AI methods for biomolecular design and other scientific applications. Design, implement, analyze, and publish original models; collaborate with research and product teams; present findings at conferences; and transfer research technology to products. The role requires strong mathematical foundations, scientific generative modeling expertise, advanced programming skills, and a strong research record.

Architects next-generation GPU and CPU systems, developing microarchitectural solutions for connectivity, coherency, power, security, memory, and protocols. Models performance and power, studies AI workloads, collaborates with software and firmware teams, supports implementation, and helps customers optimize hardware performance. Requires advanced computer or electrical engineering expertise, substantial industry experience, strong coding and algorithmic skills, and understanding of LLMs and chip architecture.

Develop production-grade integrations for NVIDIA Grove across AI frameworks including Dynamo, llm-d, Ray, and PyTorch. Build adapters, plugins, operators, runtime components, reference workflows, and developer tooling. Optimize distributed multi-node and multi-GPU training and inference, improve Kubernetes observability, define APIs, and troubleshoot containers, networking, scheduling, CUDA, and framework behavior. Collaborate with engineering teams and open-source communities to upstream maintainable changes and ensure compatibility across rapidly evolving dependencies.

Lead the design and optimization of GPU-cluster infrastructure supporting AI and ML research. Partner with researchers, data engineers, and DevOps teams to identify bottlenecks, improve researcher efficiency, and build scalable, highly available systems. Drive infrastructure roadmaps, optimize resource utilization, define performance metrics, and oversee distributed training operations across HPC environments. The role requires deep expertise in accelerated computing, storage, scheduling, networking, containers, cloud platforms, and AI/ML frameworks.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Principal-level software engineering leader responsible for architecting, building, and operating reliable enterprise AI applications and agent infrastructure. The role spans frontend, backend, distributed services, data flows, deployment, observability, testing, release engineering, security, and incident response. Responsibilities include developing reusable orchestration, memory, evaluation, telemetry, and policy capabilities; operationalizing NVIDIA AI technologies; defining secure agent collaboration architecture; mentoring engineers; and partnering cross-functionally to deliver production-grade AI systems.

Deploy, manage, validate, and optimize large-scale AI compute and HPC infrastructure in Linux environments. Serve as a technical liaison among NVIDIA, partners, and enterprise customers; lead planning through implementation, document handoffs, transfer knowledge, troubleshoot hardware and software, and provide feedback for product and architecture improvements. The role involves networking, system design, automation, benchmarking, cluster provisioning, and customer-site travel up to 30%.

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

Build NVIDIA’s AI-native TensorRT framework using agentic workflows and multi-agent orchestration. Architect scalable systems for AI-generated software, prototype state-of-the-art deep learning capabilities, and deliver high-performance inference for language, diffusion, audio, vision, and multimodal models. Optimize Python orchestration and C++ engine implementations for latency and throughput while translating customer needs into production-quality solutions.

The role involves engaging with AI-native companies to optimize their AI platforms using NVIDIA technologies, focusing on architecture, model performance, and collaboration with technical teams.

Own the strategy, roadmap, and delivery of agentic AI platform products that generate, optimize, evaluate, and deploy CUDA kernels. Partner with engineering, research, customers, and ecosystem leaders to develop coding-agent workflows, training data, evaluation benchmarks, compiler and runtime integrations, multi-agent orchestration, and continuous improvement capabilities for GPU-accelerated inference and training.

Leads the research agenda and architectural direction for large-scale AI communication systems across GPUs, DPUs, NICs, and storage. Conducts original research in RDMA, NVLink, and GPUDirect; drives hardware-software co-optimization; integrates networking into AI serving frameworks; publishes findings; influences industry standards; and mentors senior engineers. The role requires translating systems research into production-grade software and defining complex cross-team technical initiatives.

Develop Pythonic CUTLASS DSL infrastructure and APIs for high-performance GPU programming. Build AST transformations, JIT compilation, debugging and profiling tools, validation systems, package distribution, documentation, and testing. Collaborate on open-source framework development while optimizing developer experience and GPU kernel delivery.

Leads the architecture, development, and productionization of scalable data and AI platforms for supply chain operations. Responsibilities include building cloud-native data pipelines, AI agents, RAG applications, data models, observability frameworks, and GenAI foundations. The role manages and mentors engineering teams, oversees delivery across concurrent initiatives, partners with business and technical stakeholders, and provides hands-on technical leadership from discovery through production rollout.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.