Artificial Intelligence Jobs in Santa Clara, CA

Develop, optimize, and ship video processing, generative video, and 3D reconstruction algorithms (e.g., NeRF, 3D Gaussian Splatting) across desktop and mobile. Integrate and deploy deep learning models, run research and evaluations, build production-ready modules, and create test environments and tools for runtime verification of video and 3D pipelines.

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

Architect and implement low-latency sensor-to-GPU data pipelines, develop system software, drivers, and APIs, work across FPGA, networking, and CUDA stacks, integrate diverse sensors into Holoscan, and debug and optimize real-time distributed systems under strict latency and throughput constraints while collaborating with hardware, firmware, and AI teams.

Lead architecture and build AI-native developer tooling and autonomous agent systems to accelerate engineering workflows. Drive platform standards for reliability, safety, observability, and cost-efficiency while partnering across teams and mentoring senior engineers.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

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.

Lead 0→1 product development for Emerald Cloud Services: define vision, conduct customer discovery, set product requirements for AI compute provisioning and management, partner with engineering and GTM teams, and launch and scale cloud services for enterprise and AI customers.

Design and evaluate machine learning algorithms focused on autonomy and perception, prototype solutions, conduct experiments, and transition prototypes into production.

The AI Engineer will design scalable APIs and SDKs for AI services, collaborate with AI research teams, and ensure API security and performance. Responsibilities include building processing systems, integrating services, and maintaining documentation.

Lead product vision and roadmaps for AI-powered collaboration experiences; manage and mentor product teams; translate AI research into intuitive user-facing features; align stakeholders and executives; drive metrics-driven product decisions and deliver enterprise-scale AI collaboration solutions.

The Principal AI Architect will design intelligent agent systems, establish technical standards, and guide development for various industry verticals within Zoom's AI framework.

Build AI agents and architect product flows for NegotiateAI, focusing on agentic AI capabilities, user experience, and data handling requirements.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

The Senior Applications Engineer will design ATE test interface solutions, oversee hardware concepts, conduct PCB layout, and support customer testing requirements.

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 optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

The role involves assisting customers with AI solutions, collaborating with teams, analyzing deep learning performance, and leveraging NVIDIA technology.

Design, build, and deploy LLM-powered tools, RAG-based knowledge systems, and agentic workflows to automate SOC integration tasks. Own reliability, evaluation, and observability for AI systems and collaborate with SOCD teams to scope, validate, and measure productivity gains from deployed automation.

Drive benchmark results for GenAI inference, optimize performance across multiple models, and establish methodologies while leading technical initiatives in GPU performance engineering.

Lead end-to-end technical programs to integrate AI (LLMs, agents, chatbots) into chip design workflows. Plan, coordinate, and drive cross-functional execution across ASIC, platform, and third-party partners, measure program metrics, manage risks, and translate user requirements into scalable production-ready AI capabilities for silicon development.

Lead technical performance analysis and optimization of large-scale AI/HPC clusters. Diagnose cross-component bottlenecks, build and maintain benchmarking suites, collect and transform telemetry into time-series dashboards, and collaborate with engineering, field teams, and customers to resolve configuration and performance issues.

As a Senior Applied AI and AI Infrastructure Engineer, you will manage AI projects, deploy infrastructure, mentor junior engineers, and apply AI for chip design.

Design, develop, and implement AI/ML models, manage AI solutions on cloud platforms, and collaborate with teams to optimize models.

Lead and architect AI initiatives, develop large-scale AI/ML systems, mentor teams, and drive engineering excellence across Altera.

Design, build, and operate scalable ML pipelines and infrastructure across cloud and on‑prem HPC. Implement MLOps tooling (tracking, registries, feature stores), CI/CD/CT, containerized GPU orchestration, model optimization (LLMs, GNNs, RL), data/versioning pipelines, monitoring, and mentor engineers to productionize ML for EDA and simulation workloads.