Artificial Intelligence Jobs in San Jose, CA

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.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core. Responsibilities include designing vector and matrix-processing engines, optimizing power, performance, area, and timing, developing detailed specifications, supporting functional verification, debugging simulations, and partnering on physical design validation. The role requires expertise in high-performance processor architecture, datapath design, vector and matrix processing, and hardware design languages.

Architects RDU-based AI accelerator platforms spanning boards, servers, racks, clusters, interconnect fabrics, power delivery, and thermal systems. Responsibilities include defining specifications, selecting components, evaluating ODM/OEM partners, modeling performance and cost, leading bring-up and qualification, investigating reliability issues, and guiding systems through production and deployment. The role also requires cross-functional technical leadership, executive communication, vendor accountability, and mentoring across hardware, software, performance, and datacenter operations teams.

Evaluates emerging AI technologies, platforms, infrastructure, and vendors through technical assessments, proof-of-concepts, benchmarks, and pilot deployments. Assesses solutions for capability, cost, risk, scalability, and business value; recommends adoption strategies; supports customers and internal stakeholders; delivers technical presentations, workshops, and demonstrations; and monitors developments in LLMs, automation, and agentic workflows.

Lead customer-facing deployments of agentic AI for chip design: onboard and integrate ChipAgents, prototype full-stack solutions (backend models to frontend), collaborate on model and infrastructure improvements, create docs and dashboards, and remain hands-on with code, prompt engineering, and workflow orchestration to drive adoption and iterate from field feedback.

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.

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 and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

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.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Research, prototype, and implement methods to improve efficiency and deployability of LLMs, VLMs, multimodal models and agents. Focus on compression, quantization, inference and reasoning optimizations, novel architectures (MoE, SSMs, Looped Transformers), gradient-free methods, and inference-time optimizations. Build evaluation pipelines, run benchmarks, and contribute to publications, open-source, and IP.

Develop, deploy, and operate agentic AI systems that drive manufacturing decisions across S&OP, material planning, production, quality, logistics, and customer deployments. Embed with cross-functional teams to model demand, supply, BOMs, capacity, and risk; build agents that generate executable plans, propagate design changes, run historical replays and live evals, and continuously evaluate frontier models and optimization methods to improve ramp speed and schedule attainment.

The Staff Technical Product Marketing and Partner Marketing Manager shapes product strategies and develops marketing content, drives ecosystem growth, and builds sales relationships while collaborating closely with partners to enhance brand presence and pipeline growth.

The Staff Software Engineer will lead the development of cloud-native AI platforms, optimizing AI workload deployment in Kubernetes environments and collaborating with cross-functional teams.

Embed with enterprise customers to design, build, and deploy production agentic AI workflows. Integrate platform with customer systems, handle security and compliance reviews, drive adoption and expansion, and feed product improvements based on field signal.

Embed with enterprise customers to deploy production agentic AI workflows: integrate data sources, identity, CRMs and APIs; handle security, SSO/SCIM, isolated VMs and compliance; drive adoption, instrument usage, and feed product improvements to scale deployments.

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.

Build AI systems that autonomously turn new model architectures into verified, production-ready kernels optimized for Etched hardware. Develop agentic tooling to generate, compile, profile, and iterate on implementations; design evals for correctness, stability, latency, and efficiency; turn profiler traces and hardware signals into learnable datasets; ship model-generated improvements and partner with architecture teams to influence hardware-software roadmap.

The Head of AI Applications will define and execute the AI strategy within SiTime, leading the development and deployment of AI solutions across various business functions while driving adoption and managing a high-performing team.

Build and operate regulator-grade, production AI-enabled full‑stack applications: real-time audit dashboards, CAAT/continuous-monitoring surfaces, and agentic human-in-the-loop apps. Own end-to-end stack from data modeling and APIs to React/Next.js UIs, ensure responsible-AI practices, data protection, audit trails, and drive adoption with non-technical auditors.

Own full sales and partnership cycle to bring voice AI to mobile service robots: engage senior robotics decision-makers, qualify and prioritize opportunities, close deals, and shape the commercial playbook while operating autonomously within a small technical team.