Artificial Intelligence Jobs in Sunnyvale, CA

Lead product strategy and multi-quarter roadmaps for NVIDIA’s AI Factory software-defined networking stack. Guide upstream development across OVN, OVS, and OVN-Kubernetes; define requirements, priorities, and architecture trade-offs; coordinate dependencies across DPU, fabric, virtualization, and engineering teams; and translate partner and operator needs into product releases, reference deployments, and community strategy.

Lead global co-marketing strategies and campaigns with AI ecosystem, Neocloud, and AI ISV partners. Develop joint value propositions, manage partner relationships, coordinate cross-functional teams, oversee budgets and vendors, measure campaign ROI, maintain program governance, enable partners, protect brand integrity, and present results to executives. The role focuses on translating Intel hardware and software capabilities into customer-centric AI and cloud solutions.

Build and own an agentic creative pipeline that generates, vets, and optimizes thousands of image and video ad variants daily: extend LLM critic feedback to multimodal assets, build fast cost-efficient video pipelines, route generative models by cost/quality, train and deploy brand-style checkpoints, implement policy pre-flight checks, and ship event-triggered ad pipelines with A/B validation in production.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.

Develop and test resilient, scalable multi-agent AI systems and RAG pipelines. Integrate agents with external tools and APIs, run LLM/RAG evaluations, write prompts, and support light model fine-tuning to improve network security product development efficiency.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Design and build full-stack AI prototypes; decompose products into model, retrieval, prompting, agents, and orchestration components; integrate APIs, databases, and cloud; define evaluation loops; perform error analysis; collaborate cross-functionally; and present results to founders and partners.

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 research and strategy to identify and translate emerging hardware and on-device AI technologies into next-generation consumer devices. Evaluate sensors, SoCs, AI accelerators and prototypes; build business cases, drive partnerships, POCs, and commercialization plans; and coordinate cross-functional R&D, engineering, and executive stakeholders to advance Samsung's product roadmap.

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.

Leads the productization, deployment, and scaling of perception and physical AI software for robotics and automation. Owns software strategy, architecture, SDK and platform development, field deployment, OTA updates, reliability, and operational excellence. Builds and scales engineering teams across perception, autonomy, infrastructure, and platform functions while partnering with algorithms, hardware, product, systems, applications, and customer teams. Represents software strategy to executives and the board.

Own Lyte’s marketing campaigns and content strategy across social media, blogs, newsletters, press outreach, and thought leadership. Translate complex robotics and Physical AI technologies into clear messaging, support product launches and go-to-market initiatives, manage content calendars, collaborate cross-functionally, and measure campaign performance. The role requires strong writing, technical storytelling, social media expertise, and comfort working autonomously in a fast-moving startup environment.

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.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

Build a scalable platform around foundation models and LLMs, architecting multi-step, stateful workflows and high-performance infrastructure to manage asynchronous, parallel queries and enable autonomous research.

Research and implement core algorithms for a self-improving reasoning engine. Develop data-efficient methods to probe LLMs, extract hidden knowledge, synthesize fragments into reliable answers, and design autonomous self-improvement loops. Publish and bring novel ideas to production.