Artificial Intelligence Jobs in Sunnyvale, CA

Design, optimize, and deploy machine learning models for resource-constrained edge devices. Focus on model compression, quantization, hardware-aware optimization, profiling, and production deployment to mobile, embedded, and accelerator platforms while ensuring on-device privacy, security, and cross-functional collaboration.

Design and deliver enterprise-grade AI/GenAI solutions on Salesforce or ServiceNow. Lead RAG pipelines, LLM workflows, NLP automation, and vector retrieval integrations. Architect scalable API/microservice integrations, ensure performance, security, and reliability, and define AI governance and guardrails. Collaborate with product, engineering, and customers through discovery, workshops, and demos to operationalize AI for Field and Customer Service workflows.

Own and improve analytics datasets and ETL pipelines powering an internal product analytics AI agent. Build retrieval, knowledge, evaluation, and backend agent workflows. Improve retrieval and answer quality, develop evaluation systems, and create user-facing features. Partner with data scientists to turn analytics needs into scalable capabilities and help set roadmap and technical direction for the data and quality side of the platform.

Lead product vision, strategy, and roadmap for Unity AI workstreams (AI Assistant, agentic experiences, playtesting). Partner with engineering, design, and ML to deliver agentic AI workflows, run rapid iterative releases, define AI experience benchmarks, use user research and market insight, and align cross-functional stakeholders to drive impactful product outcomes.

Lead design and delivery of scalable cloud services and full-stack features for a next-generation game creation platform. Build performant front-end and backend systems, optimize cloud scalability and performance, maintain CI/CD, monitoring and databases, apply agentic AI tools in development, drive technical direction, and collaborate with AI, infrastructure, and data teams.

Support AI product development by conducting user research, experimenting with LLMs and AI models, designing evaluations, translating insights into product requirements, and owning features through development, launch, and iteration using quantitative and qualitative signals.

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.

Apply machine learning to real-time graphics and interactive experiences: develop models from latest research, prototype applications, collaborate across teams, and productize neural graphics features for gaming and visualization.

Lead and grow a mobile engineering team (iOS/Android) for Search & Discovery. Define mobile architecture, integrate ML features (on-device and server-side), drive cross-team collaboration, own performance and quality targets, and mentor engineers while shipping high-impact features.

Plan, coordinate, and execute conferences, field events, and webinars end-to-end. Manage timelines, budgets, vendors, speaker logistics, onsite setup, lead capture and CRM handoffs, and post-event reporting. Use AI tools and automation to draft copy, speed workflows, and ensure timely lead tagging and pipeline contribution.

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 end-to-end design, build, and operation of production enterprise AI systems embedded with business teams. Rapidly prototype, deploy, and measure AI solutions using LLMs, vector DBs, and Kubernetes, then productize reusable platform capabilities, ensure observability/CI/CD/security, and mentor engineers while driving enterprise adoption and measurable business impact.

The Agentic AI Systems Engineer will build infrastructure for GenAI applications, focusing on secure and scalable agentic systems, overseeing their lifecycle, and integrating AI capabilities into workflows.

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.

Design, develop, and maintain generative AI and cloud-based AI solutions using Microsoft Azure and Power Platform tools. Collaborate with cross-functional teams to gather requirements, build scalable AI applications, deploy and manage models with Azure Machine Learning, test/debug, document designs, and provide training and user support.

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 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 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.