Artificial Intelligence Jobs in Santa Clara, CA

The role involves researching AI system architecture, developing performance models, analyzing AI workloads, and guiding design decisions while collaborating across teams.

Seeking a Principal AI System Architect to lead research and design next-gen AI system architectures, optimizing performance for AI workloads with a focus on memory capacity and communication.

Develop and maintain AI networking communication frameworks, production benchmarking tools, and automation for large-scale AI training/inference. Enable models in benchmarking infrastructure, analyze end-to-end performance, perform large-scale parameter searches, and collaborate with networking and hardware teams to co-design features and optimize system performance.

The role involves researching AI system architecture, developing performance models, making design decisions, and leading architecture strategies. It requires collaboration across teams to evaluate AI workloads and scalability issues.

The role involves architecting and leading the development of a neuromorphic AI execution stack, focusing on firmware, runtime, and performance infrastructure while collaborating with multiple teams for integration and optimization.

The AI Research Engineer designs and optimizes AI systems for enterprise automation, collaborating across teams to improve efficiency and decision-making.

Responsible for architecting AIOS solutions, leading proof-of-concepts, collaborating with engineering teams, defining roadmaps, and delivering project requirements.

Research and implement methods to transfer human-object interaction data into robotic dexterous manipulation policies. Build large-scale data pipelines, generative embodied-AI models, unified cross-embodiment training, and sim-to-real deployment; validate on real multi-fingered or humanoid hardware and produce a technical manuscript or demo.

Design and implement high-performance, scalable inference systems for LLMs and VLMs across cloud, edge, and hybrid platforms. Develop and optimize custom kernels for hardware accelerators (GPU/NPU/DSP), integrate tensor/model parallelism, caching, and quantization, and collaborate with system and hardware teams to deploy production-grade AI inference.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

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

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 discovery, prototype, and productize ML/AI and edge computing innovations. Build proofs of concept, collaborate with cross-functional teams, engage customers/partners for co-innovation, influence product roadmaps, and evangelize solutions to drive commercialization and market adoption.

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 internal IT platforms to improve availability, scalability, and efficiency. Automate incident response, capacity planning, performance tuning, and integrations across enterprise applications. Collaborate with engineering and internal teams to define architectures, standards, and documentation for large-scale distributed systems. Support on-call, alerting, and reliability practices.