Top Artificial Intelligence Jobs

Research and develop AI/ML models for fault-tolerant quantum computing, including quantum error correction, syndrome decoding, logical operation synthesis, and real-time calibration. Build open models, datasets, and benchmarks using simulated and hardware-derived data. Collaborate with quantum hardware, software, product, and engineering teams to integrate low-latency AI solutions into quantum systems. Communicate findings through research publications and industry partnerships.

The role involves developing AI applications for VLSI design using machine learning and related technologies. Duties include infrastructure design, AI solution implementation, collaboration with teams, and maintaining design databases.

Design and deliver high-performance, area- and power-efficient ASIC/GPU micro-architecture and RTL. Develop agentic AI solutions to craft micro-architecture, implement RTL, and produce fully verified, synthesis/timing-clean designs. Collaborate with designers and tool owners, and build LLM-powered engineering assistants, multi-agent systems, and multi-modal dialogue tools to accelerate chip design workflows.

Primary point-of-contact for a customer, providing on-site and remote HPC, Ethernet, and AI infrastructure support. Troubleshoot Linux systems, networking protocols, and interoperability; reproduce and resolve complex issues; collaborate with engineering, marketing, and support; document support methodologies and improve processes.

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

Design and implement high-performance Vision AI pipelines and large-scale distributed services for processing video, image, and 3D data across edge and cloud. Build multi-modal perception, use simulation/synthetic data for testing, profile and optimize GPU-accelerated inference, collaborate with research and product teams, drive code quality and reviews, and mentor engineers to productionize vision models at scale.

Serve as the embedded systems expert for NVIDIA Physical AI partners, integrating sensors, deploying and optimizing robotics foundation models on Jetson-class edge hardware, guiding customers from prototype to production, and delivering reference implementations and workshops.

Lead technical programs with hyperscalers and OEM/ODMs to deliver AI datacenter infrastructure from design through production. Define schedules and milestones, translate customer requirements into cross-functional plans, resolve issues, manage qualification/validation, monitor factory production and yields, and communicate program health to customers and leadership. Champion post-deployment sustaining support and coordinate global teams to ensure timely delivery and customer acceptance.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Own go-to-market strategy and positioning for NVIDIA desktop AI products. Lead cross-functional teams through planning, launch, and post-launch optimization. Coordinate OEM and channel messaging, train regional sales, collaborate with product/sales/marketing, and drive analyst/influencer relations to grow pipeline and sell-through.

Lead architecture, design, and development of hardware-accelerated AI networking software (BlueField DPU, ConnectX). Drive deep technical engagements with hyperscalers for design-in, bring-up, performance tuning, failure analysis, and production hardening. Partner cross-functionally to turn customer needs into product features, tooling, and roadmap items while improving performance, reliability, and debuggability across stacks.

Lead robotics training data strategy: define formats and quality standards, oversee data collection/annotation, partner with researchers and external providers, build data acquisition plans, and deliver large-scale datasets and tooling to support robotics foundation models.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Architect and demonstrate simulation workflows using DSX Sim and Omniverse libraries to help partners and customers build, validate, optimize, and operationalize AI factory digital twins. Drive integration of Omniverse libraries, develop reference architectures, prototypes, benchmarks, and sample workflows, and coordinate with sales, product engineering, and partners to resolve blockers and advance adoption.

Lead partner engagements to design, prototype, and deploy production-grade agentic AI systems on NVIDIA GPUs. Architect multi-agent workflows, RAG, tool use, planning, memory, evaluation, and guardrails; build PoCs, benchmarks, and reference architectures; optimize performance and cost; guide model customization and post-training workflows; use agent harnesses and translate findings to product and engineering teams for platform improvement and field enablement.

Analyze, profile, and optimize AI training workloads on NVIDIA GPUs across the hardware/software stack. Implement production-quality software, build MLPerf training submissions, model workloads in simulators, and develop automation tools to identify and resolve performance bottlenecks.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Enable end-to-end agentic AI GPU deployments on NVIDIA RTX and DGX by profiling and optimizing performance, contributing to open-source LLM/GenAI projects, delivering trainings and sample code, collaborating with drivers/architecture teams, and mentoring junior engineers.

Lead developer-focused performance optimization for RTX and DGX SoC platforms: collaborate with partners and internal teams to optimize CPU, AI, and 3D-graphics workloads, profile and debug CPU/GPU performance, influence next-gen SoC design, and mentor junior engineers. Occasional travel for conferences and partner site visits.

Lead product marketing for AI infrastructure: analyze market and competitive signals, develop positioning and messaging, create content (blogs, webinars, tutorials), support product launches, and partner cross-functionally with engineering, sales, and GTM teams to showcase NVIDIA platform value.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.