Artificial Intelligence Jobs in Santa Clara, CA

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

The Director of AI will lead AI and data engineering teams, define and execute AI strategy, and ensure effective partnership across functions, focusing on scalable AI solutions and responsible AI practices.

Design and build scalable EDA flows and AI/LLM-driven automation for 3DIC, interposer, and RDL systems. Implement flow-generation, debugging assistants, root-cause analysis, and platform integration using C++, Tcl, Python, and Java. Collaborate with engineering teams to validate flows, automate constraint-driven designs, and address large-scale, high bump-count and multi-region routing challenges to deliver production-quality solutions.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The intern will design and implement AI-driven automation workflows to enhance the silicon testing process, collaborate with engineers, and document automation recipes.

Drive product strategy and execution for Cloud Provider and AI Networking segments. Collaborate with teams to define product requirements, assess market opportunities, and develop go-to-market strategies for Tier 1 and Tier 2 customers.

Assist in deploying and monitoring Cerebras AI infrastructure, perform troubleshooting, collect telemetry, and learn through shadowing senior engineers.

Design, develop, and productionize scalable AI tools for systems engineering in autonomous vehicles, integrating data for traceability, and building backend services.

Lead vision, strategy, and execution for AI-powered support products. Own roadmap, customer discovery, launch, adoption, and continuous optimization. Partner with engineering, data science, UX, and support to deliver generative AI, agent-assist, and autonomous support solutions that improve CSAT, reduce MTTR, increase productivity, and lower operational costs.

Lead strategy, roadmap, and delivery of AI-driven networking products using ML, LLMs, generative and agentic AI. Translate customer needs into product requirements, define KPIs, align cross-functional teams, drive go-to-market and thought leadership, and mentor product managers.

Lead product strategy and roadmaps for inference tools, SDKs, and libraries that enable developers to deploy and optimize GenAI models on NVIDIA GPUs. Partner with internal and external developers, engineering, leadership, and marketing to prioritize features, drive product delivery, and define go-to-market plans focused on performance, cost, and safety.

Design and validate high-bandwidth interconnects for AI clusters across copper and optical media. Define architectures, perform link and channel budget analysis, lead qualification and reliability testing, develop validation plans for transceivers and cables up to 1.6T, and coordinate with suppliers and manufacturing.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead developer relations for NVIDIA's local AI ecosystem: manage strategic partner portfolio, provide technical guidance, drive adoption of NVIDIA accelerated computing and runtimes, define joint plans and success metrics, enable partners with integrations and content, represent partner needs internally, and evangelize local AI at events.

Lead technical discovery and demos for enterprise prospects, translate pain points into LLM-powered solutions, present architecture and value, and feed customer insights to Product, Marketing, and R&D to shape the platform and sales strategy.

Join the Fury Team as an AI Engineer to develop AI models for autonomous systems, designing architectures and integrating ML for real-time applications.

The AI Researcher will lead research in embodied intelligence for defense systems, focusing on reinforcement learning, vision-language modeling, and building scalable AI models for autonomous applications in real-world environments.

Build and productionize AI applications integrating LLMs and vision models into an edge-to-cloud robotics platform. Fine-tune and quantize models, deploy to NVIDIA Orin/Jetson and cloud GPUs, integrate Vector DBs/RAG/agent frameworks, and create data pipelines to collect and version sensor data for retraining.

The role involves developing and productionizing security detection models, collaborating with security research, and shaping engineering culture as a founding team member.

Design, implement, and evaluate machine learning and deep learning algorithms—especially ASR for live captioning—using C++ and Python. Build and optimize Conformer, RNN-T, Transformer encoder, and beam-search based systems, perform distributed GPU training, testing, debugging, and documentation. Work with large-scale computing frameworks and adapt ML technologies to VR/AR, robotics, and interactive products. Resolve complex problems, define methods for new assignments, and may coordinate other personnel.