Artificial Intelligence Jobs in Santa Clara, CA

Lead AI software resiliency development, implementing features for reliability and fault tolerance in large-scale AI supercomputers, requiring hands-on coding and collaboration.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Lead presales solutioning for Data and AI opportunities using Snowflake, design architectures, conduct workshops, and drive AI-infused data solutions.

As a Senior Hardware Systems Engineer, you will lead AI rack and cluster infrastructure designs, manage deployments, and collaborate with cross-functional teams to optimize data center solutions.

As an AI Algorithm Developer, you'll design and implement machine learning solutions for semiconductor processing, focusing on predictive models and data analysis.

Own AI Cloud strategy and roadmap to showcase hardware advantages, define served models and pricing/tokenomics, write technical PRDs, drive cloud features for enterprise inference, incubate solution architectures, and align product with sales and partner motions to accelerate deals.

Lead the strategy for AI analytics products, building AI-powered workflows for performance diagnostics and insights. Collaborate with teams to enhance data-driven decision-making and product health monitoring.

The Senior Applied AI Engineer will build AI-driven interactive product demos, collaborating with design and engineering teams to create intelligent multimodal experiences.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

As a Senior Formal Verification Engineer, you will develop verification strategies and environments for AI SoCs, ensuring the correctness of digital designs through formal verification methods and collaboration with engineering teams.

The Research Engineer will develop AI algorithms for autonomous systems, collaborating with teams on end-to-end solutions and data-driven models.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Founding iOS engineer to own end-to-end consumer iPhone app: architecture, hardware pairing, media capture, onboarding, core user surfaces, analytics, and App Store releases. Set iOS architecture and coding standards, drive high-quality TestFlight/App Store release cycles, and collaborate with Android, hardware, and backend teams while growing the iOS team.

Lead development of the application and cognitive layer for humanoid robots: implement behavior trees and intent-to-action orchestration, build voice applications and multi-modal fusion, integrate robotics capabilities and edge/cloud AI services, and ensure production readiness with CI/CD, telemetry, and monitoring.

Work on client-side AI for Windows/Linux PCs: collaborate across teams and partners to optimize AI inferencing pipelines, perform end-to-end performance analysis, and implement compute/memory optimizations (quantization, pruning, distillation) to fit large models on edge devices.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

Lead AI search, SEO, and SEM strategy for Altera: optimize content and technical SEO, manage paid search campaigns, measure AI citation metrics, and coordinate cross-functional reporting and execution to drive organic and paid search growth.

Lead the AI Center of Excellence operating model as the CAIO's strategic chief of staff. Own executive cadence, governance, resourcing, cross-functional dependency management, partner delivery discipline, portfolio health tracking, and executive materials to accelerate decisions and shipped business value.

Lead development and operation of a governed data plane (certified data products, semantics, lineage, registries) powering BI and agentic AI. Build lakehouse, vector, and graph retrieval foundations; deploy agentic workloads for metadata generation, entity resolution, quality scoring, and unstructured classification. Manage AI Data Engineers, set standards, and drive cross-functional collaboration to ensure data reliability, certification, and production-ready AI platforms.

Lead design and delivery of an agentic AI Fabric: runtimes, orchestration, model gateway, and MCP/A2A integration spine. Own agent identity/entitlement, audit and GxP/Part 11 compliance, shared eval harness, and manage AI Harness Engineers. Drive architecture, standards, and enterprise adoption of agentic workflows and safe sandbox-to-certified promotion processes.

Lead development of local AI agent frameworks and optimized runtimes on Windows for GeForce RTX PCs. Ensure secure, sandboxed execution and privacy-aware inference routing, collaborate with AI research and driver teams, mentor engineers, and produce production-ready code for autonomous, always-on desktop AI assistants.

Lead development of local AI agent frameworks and runtimes on Windows for GeForce RTX PCs. Optimize GPU-accelerated LLM inference, enforce policy-based privacy and sandboxing, collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.