Artificial Intelligence Jobs in San Jose, CA

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

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

Lead the development of large-scale video foundation models for humanoid autonomy, focusing on training strategies and model evaluation for real-world applications.

Develop and enhance visuomotor manipulation policies for humanoid robots, focusing on real-world deployment and integration with other systems.

Develop learning systems using reinforcement learning for autonomous robots, focusing on policy optimization and real-world application.

The Helix AI Engineer, Modeling will design core model architectures for AI systems, focusing on multimodal inputs and improving model capabilities for humanoid robotics.

The Helix AI Engineer will design, train, and deploy large-scale generative models, focusing on diffusion modeling for robots, while enhancing robot autonomy through perception and world modeling.

As a Helix AI Engineer focused on pretraining, you will design and train foundation models using multimodal data, improve generalization strategies, and optimize distributed training pipelines.

Intern will design and optimize software to deploy large-scale AI models on production vehicles, profile and improve inference across compute, memory, and I/O, reduce latency and power use on embedded automotive platforms, and deliver production-ready, fleet-scalable performance optimizations.

The Risc-V Architect will analyze AI workloads, design custom RISC-V cores, and lead the architecture efforts at Tenstorrent, improving CPU performance.

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.

Design and oversee datacenter-scale AI infrastructure architectures across networking, compute, storage, and Kubernetes ecosystems. Lead prototyping, testing, performance optimization, and lifecycle refinement for scalable AI products. Collaborate with hardware, software, security teams, customers, and partners to develop proofs of concept and MVPs. Produce technical documentation, user guides, whitepapers, blogs, and curricula while translating complex systems concepts for diverse audiences.

The Innovation Lead will identify, prototype, and commercialize transformative AI, multi-agent, and edge-computing solutions for intelligent networking. Responsibilities include building working proofs of concept, coding and architecting prototypes, partnering with engineering and product teams, engaging customers and partners, influencing product roadmaps, participating in standards and open-source communities, and evangelizing technical innovations through thought leadership and industry presentations.

Designs, deploys, maintains, and tunes large-scale HPC and AI clusters. Responsibilities include workload scheduling, infrastructure automation, CI/CD pipelines, monitoring, logging, alerting, storage and networking deployment, bottom-up troubleshooting, methodology documentation, and R&D support. The engineer works across hardware, operating systems, GPU computing, virtualization, cloud platforms, and software stacks to deliver performant, reliable self-service infrastructure.

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.

Supports consulting clients in identifying, prioritizing, and implementing Generative AI solutions. Responsibilities include facilitating workshops, analyzing requirements and processes, supporting proof-of-concepts, exploring data, developing documentation, testing solutions, tracking project plans, translating business needs into technical specifications, and delivering GenAI training. The role emphasizes Microsoft AI technologies, prompt engineering, Agile and SDLC practices, continuous AI learning, and client collaboration.

Provides legal counsel for complex commercial, software, SaaS, cloud, intellectual property, and technology transactions. Advises stakeholders on AI governance, regulatory developments, vendor agreements, data usage, licensing, and emerging technology risks. Drafts and negotiates contracts, maintains legal playbooks, delivers training, and partners with business, engineering, IT, cybersecurity, compliance, procurement, and executive teams on domestic and international matters.

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 maintain end-to-end mobile trading systems for iOS and Android. Ship production-quality, well-tested code, apply blockchain and crypto knowledge, and integrate AI tools to improve engineering workflows. Break down projects, collaborate cross-functionally, conduct and act on code reviews, mentor teammates, and participate in hiring and onboarding. Influence product decisions through data-driven insights and provide domain expertise for mobile trading initiatives.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

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, deploy, and maintain on-prem AI infrastructure and agentic systems. Build GPU clusters, model serving, vector DB-backed RAG pipelines, fine-tune and deploy models, implement governance (MCP), and automate CI/CD and monitoring to integrate AI capabilities into enterprise workflows.