Artificial Intelligence Jobs in Sunnyvale, CA

This role involves designing and exporting AI frameworks, enhancing product capabilities, collaborating with teams, and directly engaging with customers.

Lead design and implementation of an AI-native web platform, owning web framework, design system, CMS, CI/CD, infrastructure and SDLC agents. Define architecture, pioneer agentic workflows, set evaluation guardrails, mentor engineers, and drive cross-team technical alignment and migrations.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

The Senior Technical Program Manager is responsible for site and data center operations, leading cross-functional programs, ensuring operational readiness, and managing metrics and KPIs for AI infrastructure.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

The Marketing AI Engineer enhances marketing with AI tools, manages application integrations, tracks AI utilization goals, and fosters team enablement in a 4-month contract role.

Lead and grow the engineering team owning AI Runtime (AIR) for managed GPU training. Define product and technical roadmap, drive architecture for distributed training at scale, build reliability and observability for multi-node long-running jobs, collaborate across product, platform, research, and customers, and hire and mentor engineering talent.

Design and validate scalable on‑prem datacenter and cluster reference architectures for enterprise AI systems. Lead multidisciplinary projects across networking, compute, storage, and cloud-native software to produce secure, resilient, and cost‑optimized hybrid AI solutions.

Own global channel strategy and execution for NVIDIA Jetson and IGX platforms. Manage distributor relationships, drive partner onboarding, pipeline creation, revenue, and partner readiness. Collaborate with marketing on channel programs, MDF-funded activities, demand generation, and performance measurement through reviews, scorecards, and dashboards.

Lead research on generative world models and synthetic data to improve training, evaluation, and generalization of end-to-end autonomous driving systems. Prototype and develop algorithms, collaborate with research and product teams to scale advances into production, define research direction and roadmaps, and drive technology transfer for safe Level 4 autonomy.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Build and scale core AI-native healthcare products across the stack, focusing on real-time audio processing, NLU/transcription, clinical documentation, EHR integrations, and distributed production systems. Drive architecture, ship features with high ownership, and operate in a fast-moving, regulated environment.

The AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

Lead product strategy, roadmap, and go-to-market for NVIDIA enterprise game development solutions. Partner with engineering, ISVs, OEMs, and sales to integrate RTX, generative AI, and developer workflows into studio pipelines. Drive positioning, demos, trade-show strategy, sales enablement, and lighthouse case studies to grow adoption among game developers and publishers.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

As an AI Research Engineer, you will work on developing AI video ad models, collaborating with teams, and contributing to research for publications.