Artificial Intelligence Jobs in Sunnyvale, CA

Architect and demonstrate simulation workflows using DSX Sim and Omniverse libraries to help partners and customers build, validate, optimize, and operationalize AI factory digital twins. Drive integration of Omniverse libraries, develop reference architectures, prototypes, benchmarks, and sample workflows, and coordinate with sales, product engineering, and partners to resolve blockers and advance adoption.

Lead partner engagements to design, prototype, and deploy production-grade agentic AI systems on NVIDIA GPUs. Architect multi-agent workflows, RAG, tool use, planning, memory, evaluation, and guardrails; build PoCs, benchmarks, and reference architectures; optimize performance and cost; guide model customization and post-training workflows; use agent harnesses and translate findings to product and engineering teams for platform improvement and field enablement.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.

Work with founders and the founding team to build AI-powered internal tools and automations, run GTM and community activities, and manage daily operations. Own content, events, scheduling, vendor logistics, and process design while using AI tools to automate and ship solutions.

Design and scale Kubernetes-native, GPU-accelerated cloud architectures for large-scale robotics simulation, synthetic data pipelines, training, and inference. Advise partners, optimize scheduling, storage, networking, and GPU utilization, and drive adoption of NVIDIA Physical AI frameworks while collaborating with product and engineering teams.

Lead the architectural design of SoC networking, SmartNIC/NIC and switch interface IPs for AI accelerators. Define scale-up/scale-out topologies, hardware offloads for collective operations, optimize transport protocols and congestion control, run performance modeling and simulations, and produce architecture specifications while collaborating across silicon, firmware, and software teams.

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.

Design, build, and maintain end-to-end integrations between Commures platform and third-party healthcare systems (EHRs/EMRs). Implement scalable, reliable solutions using LLMs and modern cloud tooling, ensure HL7/FHIR and HIPAA compliance, troubleshoot production issues, mentor junior engineers, and drive integration platform architecture and monitoring.

Lead a research team focused on enhancing large-scale machine learning models' efficiency and performance, developing novel algorithms and collaborating with engineering teams to implement breakthroughs.

Lead design and ownership of an AI-native web platform: web framework, design system, CMS, CI/CD, infrastructure, rendering and edge delivery. Define architecture and agentic SDLC, build evaluation guardrails, mentor engineers, and drive platform-wide technical alignment and migrations.

Build and scale web platform infrastructure (CI/CD, deployment automation, secrets management, edge delivery) for databricks.com. Implement automated accessibility testing and centralized consent management, unify frameworks into a shared design system, and operate within an AI-native, agent-assisted SDLC to drive platform features to production.

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.

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.

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.

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.

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.

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.

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

Design and build an AI-driven account management and optimization platform using LLMs and agentic systems. Create diagnosis and recommendation models, ensure reliable long-running services with observability and safeguards, and collaborate with ad delivery and product teams to improve advertiser performance and revenue.

Manage and curate the AI models portfolio, collaborate on model launches, evaluate model quality, and drive product marketing initiatives.