Artificial Intelligence Jobs in Santa Clara, CA

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.

Develop and optimize high-performance Vision AI systems, pipeline implementations, and collaborative projects, leveraging computer vision and deep learning for real-world applications.

The AI Compiler Engineer will innovate in AI performance, optimize compiler processes, and collaborate on hardware/software co-design, advancing AI technologies for datacenter applications.

The role involves architecting and optimizing AI inference systems, developing GPU kernels, and contributing to benchmark methodologies, requiring substantial experience in performance engineering and various programming technologies.

Design scalable multimodal data pipelines, develop algorithms and systems for training and inference efficiency, optimize large-scale distributed training, contribute to open-source GenAI infrastructure, and collaborate with research, product, and infra teams across the model lifecycle.

Lead an enterprise AI product pod to define use cases, KPIs, scope, and adoption; coordinate cross-functional stakeholders (Legal, Quality, IT/Security); ship LLM/agent/RAG solutions into real workflows; produce reusable Fabric assets; manage roadmap, telemetry-driven adoption, and portfolio reporting.

Lead a team to define and deliver AI-enabled engineering automation and SDLC tooling. Own roadmap, integration, governance, and adoption of AI agents, copilots, and CI/CD automation to improve developer productivity, quality, and reliability across the organization.

Build domain data products and pipelines for AI consumption: create retrieval-ready, semantically annotated, contract-governed assets; deploy agents for metadata, entity resolution and classification; ensure data quality, lineage, certification, and reuse; collaborate with domain stewards, business teams and IT to model, store and serve structured and unstructured data for retrieval and modeling.

Lead design, development, and operationalization of enterprise Generative AI solutions (LLMs, RAG, agentic AI). Architect scalable cloud deployments, implement GenAIOps, observability, and secure CI/CD pipelines. Mentor engineers and partner with product, risk, and security teams to deliver production-ready, compliant AI systems.

Lead AI technical strategy and roadmap across product portfolio. Drive design, development, and production deployment of large-scale ML and generative AI models (including LLMs), mentor teams, align stakeholders, mitigate risks, and ensure timely delivery of AI capabilities.

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.

Join a talent network connecting senior/staff applied AI scientists to VC-backed startups. Responsibilities include researching and developing ML methods, fine-tuning foundation models, designing experiments and evaluation frameworks, building prototypes, collaborating with engineering/product to productionize models, improving model robustness and efficiency, curating datasets, investigating failures, mentoring peers, and communicating results to stakeholders.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Join a talent network connecting founding AI engineers with VC-backed startups. Responsibilities include designing, building, and deploying ML/DL models, creating scalable data pipelines, optimizing real-time and batch model deployments, developing RAG and LLM systems, and aligning AI strategy with founders and product goals.

Join a VC-backed startup network connecting Director/VP-level AI/ML leaders to portfolio companies. Lead AI strategy, build and scale ML teams, deploy and improve production ML systems, set model quality and responsible AI standards, partner with product and engineering, oversee data and MLOps, evaluate models and tooling, and communicate strategy to executives and boards.

Design and ship LLM-powered agentic AI systems and RAG pipelines, build multi-step reasoning workflows, improve model behavior (accuracy, latency, cost), integrate tools and knowledge retrieval, and collaborate on hardware-aware AI optimizations with FPGA/ASIC teams for production platforms.

Design and ship advanced machine learning systems from research to production. Lead experimentation, training, fine-tuning, and evaluation of deep learning models at scale, implement distributed and mixed-precision training on accelerator hardware, ensure observability and safeguards, and translate research advances into robust, deployable solutions.

Optimize throughput, latency, and cost for training and inference of large neural networks. Work across kernel, compiler, and distributed-system levels, profile and measure performance, mentor engineers, and ship production-grade ML system improvements.

Design, research, and ship production-scale ML and deep learning systems. Lead experiments, train and fine-tune models at scale, move prototypes to production with observability and safeguards, and evaluate/adapt cutting-edge ML research for real-world impact.

Define and lead prompt design strategy, agentic workflows, and LLM application architectures. Build reusable design patterns, developer tooling, and evaluation frameworks. Ship LLM-powered products to production, ensure responsible AI practices, mentor engineers, and guide technical direction across teams.

Design, execute, and operationalize fine-tuning workflows for large language models (supervised, preference-based, RL). Build dataset pipelines, run distributed training on GPU clusters, evaluate models rigorously, and collaborate with cross-functional teams to productionize LLM solutions while mentoring junior engineers.

Design, build, and deploy AI/ML tools and LLM/agent-based systems for GeForce NOW SRE. Transform production signals, metrics, and logs into actionable intelligence, automate root-cause analysis, manage large-scale data pipelines, and operate ML tooling on Kubernetes and AWS with monitoring and visualization (Grafana).

Develop and own AI/ML/DL methodologies to estimate pre-silicon power and improve GPU energy efficiency. Build data pipelines, tools for dataset gathering/annotation, LLM training/fine-tuning, RAG pipelines, vector DBs, agentic frameworks, visualizations, and analytics to drive power optimization and debugging across hardware and infrastructure teams.

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.