Artificial Intelligence Jobs in Sunnyvale, CA

Deploy, optimize, and integrate LLMs and multimodal models on local GPU/ARM64 hardware. Develop custom CUDA kernels, tune inference (TTFT, tokens/sec), connect backends to orchestration layers, build prototypes and frontends, and implement device communication protocols for local AI compute.

Build and productionize internal AI-powered tools and data systems. Implement and maintain infrastructure, data pipelines, deployments, monitoring, and access controls. Integrate third-party AI services, ensure data quality, document systems, and collaborate with stakeholders to deliver maintainable solutions.

Lead architecture and integration of RISC-V-based AI compute clusters and vector engines. Define ISA extensions, memory subsystems, interconnects, task-mapping and scheduling, and performance/debug features. Collaborate with compiler, ML runtime, RTL, verification, and firmware teams to optimize performance-per-watt and scalability across silicon generations.

Design and optimize high-performance compute kernels for AI primitives (GEMM, attention, convolution), profile and tune across heterogeneous hardware, collaborate with compiler/runtime teams (Triton, PyTorch, SYCL), prototype precision formats, and contribute micro-architecture feedback while writing reusable C++/CUDA/Triton/MLIR code.

Verify chip-level and performance behavior of custom ASICs: design end-to-end test flows with architecture team, run simulation-based verification, implement UVM environments, use constrained-random and functional-coverage methodologies, and coordinate vendors/contractors.

Lead and coordinate cross-functional execution of rack-scale platform design from architecture through design release. Maintain master schedules, track critical-path items and risks, run design- and validation-readiness reviews, coordinate with systems engineering and external CM/JDM partners, ensure supply-chain visibility for long-lead items, and prepare executive updates on milestone health and resource gaps.

Design and operate petabyte-scale end-to-end data pipelines for autonomous driving: onboard upload, cloud preprocessing, dataset production, and model training input. Build data cleaning, annotation QA, lineage, versioning, metadata management, and high-throughput distributed processing. Optimize I/O, memory, transmission, and containerized deployments; support cross-team collaboration and algorithm iteration.

Build production-ready backend components that interface directly with LLMs, create tooling to automate LLM-in-the-loop testing, design and validate LLM-based solutions using real biomedical data, and develop metrics to measure model performance and reliability under production load.

Lead Marvells enterprise AI tools strategy and drive adoption end-to-end: evaluate and procure tools, build vendor partnerships, design enablement and change management programs, advise executives, partner with HR/IT and AI Governance, and hire and scale the Enterprise AI Transformation team to embed AI responsibly across the company.

Design, build, deploy, and operate large-scale GPU cloud infrastructure and tooling for AI training and inference. Perform performance analysis, capacity management, monitoring, automation, incident response, and lifecycle support for distributed multi-GPU/multi-node systems.

The Product Manager for Physical AI Agents will define the product roadmap, translate AI capabilities into features, and ensure quality evaluation for robotic agents while collaborating with research and engineering teams.

The role involves synthesizing, placing, and routing high-speed designs, collaborating with RTL teams, and optimizing designs for power/performance area.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

The Senior Software Engineer will work on optimizing open-source LLM inference, enhancing performance on NVIDIA GPUs, and collaborating on production-ready solutions.

The role involves building AI applications, serving as a technical expert for partners, and developing prototypes using NVIDIA's Generative AI platforms.

The role involves collaborating with customers to optimize AI workloads, leading proof-of-concepts, and developing software for NVIDIA's AI frameworks while ensuring clear communication and team collaboration.

Lead development of XR-based teleoperation and data-collection systems for robotics, support partner onboarding and integrations, write docs and sample code, triage partner issues, review partner OSS contributions, optimize CloudXR/Omniverse/Isaac performance, and coordinate cross-functional NVIDIA teams to drive partner adoption.

Lead technical performance analysis and optimization of large-scale AI/HPC clusters. Diagnose cross-component bottlenecks, build and maintain benchmarking suites, collect and transform telemetry into time-series dashboards, and collaborate with engineering, field teams, and customers to resolve configuration and performance issues.

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.

The CX Manager will own the ERP integration domain, providing problem-solving expertise for complex customer issues and ensuring efficient issue resolution related to ERP systems and AI operations.

The role involves driving the architecture and evolution of the Search product at Databricks, collaborating across teams, mentoring engineers, and ensuring high-performance systems for AI applications.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

Responsible for building AI frameworks for recommender systems on NVIDIA GPUs, developing product strategies, and collaborating with customers to enhance ML lifecycle products.

Lead the architectural development of AI agent frameworks for Windows, optimize performance on NVIDIA GPUs, and mentor engineers in deployment best practices.

The Software Engineer will build AI-powered chatbot workflows, own features end-to-end, and collaborate in a fast-paced environment. Ideal candidates have a strong engineering foundation and experience with AI development tools, focusing on enhancing software processes.