Top Artificial Intelligence Jobs

Lead enterprise sales strategy for NVIDIA’s Physical AI and digital twin solutions across industrial manufacturing. Develop customer and partner relationships, identify factory and warehouse applications, curate sales plays, enable global teams, support lighthouse accounts, track opportunities, and provide market feedback to product teams. The role requires deep experience in enterprise software business development, partner solutions, industrial technologies, consultative selling, and cross-functional leadership.

Engage leading academic AI labs researching foundation models, LLMs, multimodal systems, reasoning, training, inference, and scalable AI infrastructure. Advise researchers on NVIDIA platforms, identify impactful workloads, track frontier research, translate academic feedback into product and platform opportunities, and support technical workshops, university programs, and research conferences. The role requires deep foundational AI expertise, hands-on experience with AI training or inference, and strong research engagement credibility.

Develop AI-driven software for VLSI and circuit design automation. Research electronic design automation techniques, build agentic AI solutions, analyze datasets, validate hypotheses, and create machine learning models and algorithms to optimize circuit and layout design outcomes. Collaborate across engineering and research teams on pre-silicon and post-silicon design, circuit optimization, and SPICE correlation.

Reviews AI-generated legal and regulatory content for accuracy, completeness, legal reasoning, and jurisdictional relevance. Conducts authoritative legal research, verifies laws and regulatory guidance, identifies errors and unsupported conclusions, and documents structured feedback. Requires expertise in Mainland China or Spain legal systems, strong written English, independent work, attention to detail, and compliance with geographic eligibility requirements. This is a fully remote, freelance project involving approximately 36 hours of work.

Lead and mentor an AI agent engineering team developing scalable, secure HR and Finance AI solutions. Drive technical strategy, project delivery, platform integration, and adoption of LLMs, RAG, and orchestration frameworks such as LangGraph. Partner with product, data science, architecture, vendors, and open-source communities while ensuring privacy, security, compliance, testing, monitoring, reliability, and performance.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Lead Deepgram’s corporate legal function, including governance, IPO readiness, financings, M&A, employee equity, international subsidiaries, and regulatory developments. Partner with Finance, People, and company leadership to build scalable legal processes for a fast-growing AI company. Establish an AI-native legal practice while advising on a broad range of startup legal matters.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

Own Jetson’s developer experience, system performance, competitive intelligence, and product roadmap for edge AI and robotics. Lead requirements and release readiness across real-time execution, low-latency networking, RDMA, memory optimization, profiling tools, SDK usability, onboarding, documentation, and sample code. Partner with engineering, customers, partners, ISVs, and developers to prioritize performance-critical features and strengthen NVIDIA’s platform leadership.

Provides advanced technical support for NVIDIA networking and AI infrastructure products, including InfiniBand, NVLink, Ethernet, GPUs, Linux, and HPC environments. Troubleshoots customer issues across installation, operation, maintenance, interoperability, and performance. Responds through multiple support channels, collaborates with Engineering and Marketing, develops support methodologies, documents solutions, and improves internal support processes. Requires extensive networking, Linux administration, virtualization, containerization, cloud infrastructure, packet analysis, and customer debugging experience.

Lead a team of AI engineers building production AI applications, agents, automation, retrieval pipelines, and model integrations for Finance. Partner with CFO organization functions to identify high-value use cases, set engineering standards, review designs and code, evaluate external AI vendors, run proofs of concept, and make build-versus-buy recommendations. Scale adoption through reusable AI components, technical patterns, and development standards.

Lead development and optimization of local AI agent frameworks and runtimes for Windows PCs with NVIDIA GeForce RTX GPUs. Build secure, sandboxed, privacy-focused agent execution and inference systems, collaborate with AI research, driver, and open-source teams, and establish deployment guidelines. Mentor engineers while delivering production-ready code using Windows systems programming, C++, Python, LLM inference pipelines, GPU acceleration, and agent orchestration frameworks.

Develop and activate integrated AI infrastructure marketing campaigns for enterprise audiences. Create technically accurate content and executive thought leadership across web, social, video, and other channels. Monitor market and ecosystem trends, refine campaign narratives, and measure performance. Collaborate with product marketing, technical, creative, communications, digital, regional marketing, and external teams while leading significant campaign workstreams.

Develop and execute public relations campaigns for NVIDIA’s enterprise AI models. Translate complex AI research and open-source model capabilities into compelling narratives, press releases, and media stories. Build relationships with reporters, researchers, partners, executives, and internal teams; secure meaningful technology coverage; coach teams on messaging; and coordinate globally to promote AI model innovation.

Leads the full lifecycle of global AI inference programs spanning software, hardware, tooling, and product teams. Responsibilities include planning, scheduling, dependency tracking, executive reporting, agile coaching, risk metrics, technical alignment, process improvement, and resolving resource conflicts. The role partners with engineering leadership to coordinate software libraries with hardware timelines and drives complex technical discussions toward documented decisions and successful delivery.

Build the developer ecosystem for NVIDIA Dynamo, an open-source distributed AI inference framework. Responsibilities include creating demos, sample code, documentation, videos, technical talks, workshops, and office hours; collaborating with inference projects and open-source maintainers; troubleshooting deployments; gathering developer feedback; influencing engineering and product priorities; and communicating progress externally. The role requires hands-on experience with model serving, Kubernetes, distributed inference, performance optimization, and developer or partner programs.

Develop GPU-accelerated techniques for generative AI training and inference, optimize algorithms and performance across NVIDIA CPU and GPU architectures, contribute to frameworks, libraries, and open-source projects, and collaborate with developers, researchers, and hardware and software engineering teams. The role focuses on LLMs, diffusion models, multimodal systems, low-level optimization, and communicating innovations through technical publications and industry conferences.

Conduct foundational research in autonomous vehicles, robotics, and physical AI. Design and implement new methods for perception, reasoning, closed-loop training, safety assurance, navigation, and human interaction. Publish original research, collaborate with internal and external researchers, open-source results, and transfer innovations to product teams. The role requires a 2026 PhD, relevant autonomy or robotics research experience, strong Python skills, machine learning framework experience, and research publications or comparable artifacts.

Provide advanced technical support for NVIDIA Ethernet switching and AI infrastructure products. Troubleshoot Linux, server, hardware, operating system, networking, installation, interoperability, and operational issues for enterprise customers. Analyze protocols and packet captures, lead critical issue resolution, collaborate with Engineering, Marketing, and Support, and develop support methodologies and documentation. The role focuses on data center, HPC, networking, and AI infrastructure environments.

Engage university researchers and laboratories to accelerate adoption of NVIDIA AI and computing platforms. Advise research teams, integrate NVIDIA frameworks into scientific workloads, identify ecosystem opportunities, monitor research trends, coordinate conferences, and translate researcher feedback into product roadmaps and enablement programs. The role requires deep AI and computational science expertise, research credibility, hands-on experience with modern AI systems, and strong relationships across academic and open-source research communities.

Lead technical engagements with OEM and deployment partners to design, validate, and deploy scalable NVIDIA AI infrastructure. Map customer workloads to NVIDIA hardware and software, co-design architectures, resolve complex technical issues, drive platform adoption, and influence product priorities. Lead AI Factory build-readiness execution, deployment validation, technical enablement, and issue resolution across large-scale GPU infrastructure deployments. Travel up to 50% is required.

Design and implement GPU performance profiling tools focused on AI applications. Build features used by CUDA developers, collaborate with NVIDIA compiler and CUDA driver teams, and influence tool design. The role requires strong C++ software engineering experience, GPU hardware knowledge, and familiarity with performance tools, with preferred expertise in CUDA kernel programming, profiling, and DWARF parsing.

Designs and optimizes NVIDIA AI and HPC infrastructure for energy-sector customers, including GPU clusters, networking, storage, orchestration, monitoring, security, and edge deployments. Partners with customers and internal teams on architecture reviews, proof-of-concepts, benchmarks, production deployments, reference architectures, and technical documentation. Advises on distributed training, inference, RAG, cluster reliability, and performance optimization while supporting strategic customer and partner engagements.

Drive technical engagements for NVIDIA’s on-premises AI Factory solutions. Advise partners, OEMs, and enterprise customers; design and validate scalable architectures; support deployments and troubleshooting; enable adoption of NVIDIA platforms; and communicate customer needs to engineering and product teams. The role requires hands-on expertise across AI infrastructure, hardware, firmware, operating systems, cluster management, workload orchestration, and system integration.

Supports customer-facing technical engagements with OEM and deployment partners, helping design, validate, troubleshoot, and deploy scalable NVIDIA AI infrastructure solutions. The role contributes to architecture, integration plans, enablement programs, deployment guides, onboarding materials, documentation, and partner adoption while relaying technical feedback to engineering and product teams.