Artificial Intelligence Jobs in Sunnyvale, CA

Develop and optimize end-to-end data pipelines for autonomous driving, ensure data quality, and support model training through efficient data management processes.

In this role, you will design and develop AI-native applications, managing the lifecycle of high-scale AI systems using LLMs and generative AI to enhance customer experiences.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Develop and optimize software for IP customers, including firmware, drivers, and APIs. Collaborate with cross-functional teams for software integration and troubleshooting.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Lead enterprise AI transformation and hyper-automation programs, architect agentic AI workflows, establish AI governance and CoE, implement LLM and RPA platforms, partner with executives to drive ROI, modernize systems, and manage distributed teams.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

The role involves deploying AI networking solutions in data centers, driving architectural decisions, troubleshooting network issues, and collaborating on customer projects for NVIDIA's technology solutions.

Design and deploy rack-scale AI infrastructure and cluster solutions for NVIDIA products. Collaborate with product teams and cross-functional engineering groups to onboard hardware/software stacks, optimize cluster deployments, and support multi-site datacenter and lab rollouts. Troubleshoot complex system failures, automate provisioning, and improve time-to-market for next-generation GPU/Tegra-based products.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Contract SRE to build, operate, and automate colo, on‑prem GPU clusters, and cloud infrastructure. Own provisioning, IaC (Terraform/Ansible), observability (Prometheus/Grafana or DataDog), incident response, runbooks, and customer-facing platform reliability.

Own and optimize the AI inference and execution layer: quantization, KV-cache, batching, latency/memory tuning, fine-tune and evaluate models, build bounded task runtimes, enable hardware acceleration and partner with systems/backend teams to ship production-grade agentic AI capabilities.

Lead end-to-end applied AI engineering projects: define requirements, design and own GCP infrastructure, oversee development and contractors, deploy production ML systems, write PRDs and architecture, drive company-wide AI rollout, training, governance, and scalable operational foundations.

Design, build, and operate large-scale, low-latency recommendation systems (retrieval, ranking, multi-objective optimization). Drive offline training, online inference, A/B testing, and metric analysis. Apply LLMs and foundation models for content understanding, user intent, and feature generation. Rapidly prototype AI/LLM ideas and own projects from MVP to production, ensuring data quality and system performance.

Lead technical direction and architecture for large-scale personalization and recommendation systems. Drive adoption of LLMs/foundation models, own retrieval-to-ranking pipelines, model lifecycle management, experimentation, and production scaling. Mentor teams and shepherd 0→1 AI platform initiatives into maintainable production.

Founding technical role to design, build, and deploy internal AI tools and agents using LLMs and retrieval systems. Partner with investors and the data team to accelerate sourcing, diligence, portfolio insights, and define CRV's AI strategy through rapid prototyping and production-grade systems.

The AI Hardware Architect will analyze and design next-generation AI hardware, focusing on microarchitecture, connectivity, and system performance for GPUs and CPUs.

Lead robotics training data strategy: define formats and quality standards, oversee data collection/annotation, partner with researchers and external providers, build data acquisition plans, and deliver large-scale datasets and tooling to support robotics foundation models.

Own and scale the Nx-based monorepo, build system, and CI/CD to make builds, tests, and developer workflows fast, reliable, and observable. Build developer tooling, local dev stacks, and production-grade developer infra with SLOs, on-call, and measurable DX improvements.

Lead end-to-end delivery of AI deployments for financial services clients: define features, translate business needs into technical specs, run agile sprints, drive adoption and change management, ensure measurable program outcomes, and expand commercial relationships while collaborating with engineers, product, design, and investment teams.

Design, build, and scale the AI platform for distributed multi-GPU training, experiment tracking, and model deployment. Optimize GPU clusters, networking (NCCL/InfiniBand), Kubernetes-native scheduling, and inference serving (TensorRT, Triton). Develop agentic automation for cluster health, CI/CD, and data curation while integrating ML lifecycle tooling and observability.

The Machine Learning Engineer will design and deploy multi-modal models while collaborating with cross-functional teams to translate research into production-ready solutions, focusing on climate change mitigation.

Develop scalable data pipelines for autonomous driving, enhancing data extraction and labeling processes, and collaborating with cross-functional teams on ML models.

Lead product marketing for enterprise agentic AI solutions: craft audience-specific messaging and value propositions, enable sales and partners, drive go-to-market strategy and launches, translate complex technical capabilities into compelling content to accelerate customer adoption.