Artificial Intelligence Jobs in Sunnyvale, CA

As a founding Solutions Engineer, you will lead technical discovery, run POCs, and ensure successful customer integration while collaborating with product and revenue teams.

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.

Build and scale backend infrastructure for Ambient AI in healthcare. Work across real-time clinical documentation, AI-driven billing automation, and clinical intelligence. Ship production systems, collaborate with clinicians and customers, maintain reliability, monitoring, and metrics, and operate across the full software stack.

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.

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.

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

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.

Design and optimize large-scale AI platform internals: inference runtimes, model serving, GPU and distributed performance, context engineering, cost optimization, release safety, observability, and production reliability. Provide technical leadership, mentor engineers, define architecture trade-offs, and ensure safe, cost-efficient, and scalable deployment of LLM and agentic systems.

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.

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 AI Accelerator Silicon Architect will define compute architecture, analyze performance, and collaborate with teams on silicon architecture for AI workloads.

Lead and drive design-phase execution for rack-scale AI platforms. Maintain master schedules, track critical-path items and risks, coordinate cross-functional design and readiness reviews, engage CM/JDM partners, ensure validation readiness, and provide executive updates on milestones, risks, and resource gaps.

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