Artificial Intelligence Jobs in Santa Clara, CA

Leads engineering for an AI-native security operations product suite. Responsibilities include guiding architecture, setting engineering and product strategy, developing talent, engaging customers, and applying emerging AI innovations. The role requires leading high-velocity teams, shaping product roadmaps, coding with AI agents, building cloud software across AWS, GCP, and Azure, and collaborating with globally distributed teams.

Leads the architecture, strategy, and implementation of applied and agentic AI capabilities for HPE private and hybrid cloud offerings. Designs production-grade AI architectures, multi-agent systems, LLMOps, AI security, governance, observability, and knowledge layers. Builds reusable AI components, optimizes large-scale AI systems, guides technology selection, and converts proofs of concept into reliable services. Influences customers and internal teams through technical workshops, reference architectures, publications, and presentations while mentoring engineers and architects.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

Support the ISG executive leader by driving strategy, operations, and cross-functional program execution. Lead strategic planning, KPI operating model, business reviews, communications, and coordination with corporate support teams to enable division objectives and strategic initiatives.

Lead design, implementation, and optimization of Google Workspace and Microsoft 365 in enterprise and FedRAMP environments. Drive AI collaboration platforms (Gemini, Claude), deploy MCP servers and Agent Executor frameworks, automate user/group provisioning, manage IAM/RBAC and Jump host security, support ConMon and FedRAMP audit readiness, and integrate collaboration tools like Zoom and Lucid.

Lead product marketing for CoreWeaves Agentic AI platform: define positioning, messaging, GTM programs, sales enablement, partner GTM, customer evidence, thought leadership, and market intelligence to drive adoption of agentic AI infrastructure.

Build a Physical AI security platform covering threat modeling, cyber risk analysis, intrusion detection, security testing, investigations, and agentic workflows. Develop full-stack features with TypeScript, React, and a typed backend language; integrate web applications, AI agents, and autonomous devices; deploy LLM-powered capabilities; and model relational domains with traceability and durable asynchronous workflows.

Designs and deploys end-to-end Physical AI and robotics solutions across data collection, simulation, model training, optimization, robotic hardware deployment, and fleet operations. Advises enterprise customers, cloud providers, systems integrators, and ecosystem partners on integrating AI workloads with IT, OT, edge, and cloud environments. Leads proofs of concept, technical evaluations, benchmarking, and solution validation while influencing Intel product roadmaps, strategy, and customer adoption.

Design, build, and operate agentic AI workflows and MCP servers for an LLM-based network operations assistant. Implement LangGraph orchestration, RAG/memory systems, evaluation frameworks, and production integrations (Kubernetes, CI/CD, containers, FastAPI). Collaborate with domain experts, ensure observability, and drive reliability of non-deterministic AI systems at scale.

Leads AI and legal technology enablement across West Coast law firm offices. Designs training curricula, academies, workshops, communications, and adoption campaigns; manages change initiatives; builds attorney stakeholder relationships; analyzes usage data; reports progress and risks; and promotes responsible, habitual technology adoption among attorneys and practice groups.

Lead quality engineering activities for aerospace defense hardware, including developing QA systems, root-cause corrective actions, inspections, statistical analysis, audits, supplier corrective actions, and continuous improvement to ensure conformance to MIL-STDs and company/regulatory standards.

Build and deploy production-grade AI applications using Python and Azure. Develop LLM, RAG, and agentic workflows; integrate enterprise platforms, APIs, databases, and Microsoft 365; and create scalable backend services. Partner with stakeholders to translate ambiguous business needs into production solutions while applying strong testing, security, observability, scalability, and performance practices.

Lead regional enterprise sales for NVIDIA’s Physical AI and digital twin solutions. Build executive customer and partner relationships, develop end-to-end solution sales plays, enable global sales teams, manage lighthouse opportunities, track market adoption, and provide customer insights to product teams. The role focuses on industrial manufacturing, factory planning, robotics, synthetic data, intelligent infrastructure, and expansion across APAC and the Americas.

Leads Wayve’s data strategy for autonomous-driving AI, including data acquisition, discovery, curation, enrichment, training, evaluation, deployment, and performance measurement. Builds the data flywheel, identifies rare and safety-critical scenarios, connects data investment to model outcomes, scales global data architecture, and leads a multidisciplinary Data organization. Partners with AI, Science, Validation, Product, Compute, Finance, and Commercial teams.

Design and build AI-powered cybersecurity platforms that ingest and normalize massive security telemetry, correlate events, predict threats, and generate explainable findings. Develop Python backend services, distributed storage systems, and AIDevOps pipelines for rapid experimentation. Integrate foundation models, agentic systems, RAG, embeddings, and graph-based models into analyst workflows. Collaborate with product teams, security experts, and engineers to deliver scalable, trusted threat detection and automation capabilities.

Design and build AI-native cybersecurity platforms that analyze large-scale security telemetry, correlate events, predict threats, and generate explainable alerts. Develop scalable distributed systems, data pipelines, AI DevOps workflows, agentic systems, RAG and embedding solutions, and model integrations. Collaborate with security researchers, SMEs, product managers, and engineering teams to create trusted automation and analyst-focused threat detection, investigation, and response capabilities.

Design and build AI-powered cybersecurity platforms that process large-scale telemetry, detect and predict threats, and generate explainable alerts. Develop scalable Python services, distributed data systems, AI DevOps pipelines, agentic workflows, RAG capabilities, and model integrations. Collaborate with security experts, product managers, and engineers to validate AI features and improve analyst workflows for security operations.

Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.

Develops technical content, interactive demonstrations, reference architectures, and enablement tools that showcase AI infrastructure and data platforms. Partners with product, marketing, engineering, sales, and partners to translate complex AI/ML architectures, RAG workflows, storage, and networking technologies into compelling technical narratives. Presents at conferences and executive briefings, leads hands-on technical showcases, supports enterprise deals, and channels customer feedback into product and developer-experience improvements.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

Build and scale an enterprise Retrieval-Augmented Generation platform using Go and Python. Develop full-stack features, backend microservices, frontend interfaces, and AI/ML integrations in partnership with NVIDIA engineering teams. Own features from concept through production, while improving system performance, testing, security, availability, and reliability. Apply hands-on expertise in RAG, LLM pipelines, and AI-driven data retrieval architectures to deliver low-latency enterprise capabilities.

Leads technical architecture and solution design for complex cloud, AI, data center, hyperscale, and WAN networking sales opportunities. Translates customer requirements into scalable solutions, leads demos and proof-of-concepts, prepares RFP responses, and engages senior stakeholders. The role develops architecture playbooks, mentors sales engineering teams, influences deal strategy, and partners with customers, hyperscalers, service providers, and technology partners globally. Travel of up to 50%, including international travel, is required.

Leads a multi-year Agentic AI transformation of the Product Development Lifecycle across Product Management, Engineering, and QA. Responsibilities include assessing current processes, designing the future operating model, managing change and executive alignment, driving implementation, defining roadmaps and KPIs, and establishing governance. The role requires strategic thinking, executive influence, cross-functional leadership, project management, and knowledge of software engineering practices within a B2B technology environment.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

Build and maintain software that integrates ML/GenAI models with Kubernetes-based edge orchestration. Develop controllers, operators, CRDs, and Helm charts to schedule and deploy AI workloads at the edge, integrate ONNX/PyTorch/TensorFlow models, use AI coding agents, contribute to open-source components, and collaborate across AI, infrastructure, and product teams to ship features end-to-end.