Artificial Intelligence Jobs in Sunnyvale, CA

Lead end-to-end intake, evaluation, onboarding, and governance for third-party AI-powered agents in the Agent Marketplace. Build risk-tiered onboarding models, governance frameworks, and lifecycle controls while partnering with product, engineering, legal, compliance, risk, and security. Present recommendations to leadership and continuously improve processes to scale the marketplace securely and compliantly.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Design, build, and deploy prompt-engineered AI agents and copilots for sellers. Create and tune prompts, connect LLMs to GTM systems (Salesforce, Snowflake, Databricks), ship production workflows, run experiments, monitor performance, and drive field adoption while ensuring security, privacy, and responsible AI governance.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Lead the architecture and development of advanced analytics, data products, predictive models, anomaly detection systems, and GenAI solutions using Engineering and Supply Chain data. Translate stakeholder needs into scalable technical solutions, manage analytics and AI roadmaps, collaborate on production deployments, and integrate solutions into business processes. Ensure data quality, governance, security, explainability, bias detection, and responsible AI practices while influencing cross-functional stakeholders and driving adoption.

Lead workload-driven AI/ML systems architecture across hardware and software. Own workload characterization, analytical performance modeling, KPI definition, and hardware performance analysis to guide SoC, memory, and HW/SW co-design decisions. Evaluate compute, memory, latency, and software bottlenecks; collaborate with CPU, compiler, runtime, and ML framework teams; present recommendations to leadership; and mentor junior engineers. The role requires expertise in processor and memory architecture, edge AI acceleration, performance modeling, and cross-functional technical leadership.

Lead the architecture, prototyping, deployment, and scaling of agentic AI platforms for NVIDIA Marketing. Translate marketing opportunities into reliable AI agents, reusable platform capabilities, and production architectures. Establish practices for evaluation, observability, security, governance, reliability, and cost optimization while partnering with engineers on integrations, tooling, retrieval, memory, orchestration, and infrastructure. Guide complex initiatives from discovery through ongoing operations and communicate technical solutions to technical and business stakeholders.

Designs, builds, and ships technical growth mechanisms across acquisition, onboarding, referrals, activation, and monetization. Owns growth budgets, tracking specifications, analytics events, experimentation, attribution, and cohort analysis. Prototypes growth hypotheses for AI products, develops business cases and go-to-market strategies, and presents data-driven recommendations to executives, clients, and partners. The role requires strong coding, analytical, communication, executive presence, and end-to-end ownership skills.

Design and implement machine learning algorithms for autonomous robotics using Python. Develop deep neural networks for end-to-end learning, 3D scene understanding, object detection, world reconstruction, and self-supervised learning. Build datasets from camera, LiDAR, and radar data; manage automated AI workflows; and support production machine learning pipelines from curation through validation. Collaborate across engineering teams and document solutions clearly.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

Lead design and delivery of SMAC workflow methodology and infrastructure to keep system specifications and manufacturing test specs aligned. Build production-grade Python pipelines, automated checks, CI gates, and agent-ready tooling that detect spec drift pre-silicon. Integrate workflows into program milestones, drive cross-org adoption across design, operations, and DFX, and apply AI where appropriate while ensuring reviewable, auditable artifacts.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

The Solutions Architect will design and build enterprise AI solutions, aid in adopting NVIDIA AI technologies, and work on deep learning applications for customers across industries.

Build and scale full-stack features for the Insights practice management platform using ReactJS, Python, and PostgreSQL. Improve reliability, observability, performance, and availability; collaborate with infra, backend, product, and design; own multi-month projects, conduct code reviews, and drive architectural improvements to support thousands of practices.

Design, build, and operate end-to-end AI-enabled revenue cycle management features. Implement integrations, improve reliability, optimize performance, and ship production LLM-powered agents with monitoring, observability, and architectural ownership.

The role involves engaging with AI-native companies to optimize their AI platforms using NVIDIA technologies, focusing on architecture, model performance, and collaboration with technical teams.

Leads integration of Wayve’s AI stack into production automotive platforms and OEM programs. Responsibilities include embedded software integration, AI model adaptation, distillation, tuning, validation, deployment strategy, and development of reusable application-layer frameworks. The director will coordinate research, product, safety, and release teams; provide technical leadership to OEM and Tier 1 customers; and build a high-performing embedded AI integration team.

Design, build, and operate large-scale, highly available Responsible AI cloud services. Lead projects, ensure production readiness and DevOps practices, integrate with Azure AI products, and collaborate with engineers, scientists, and stakeholders to deliver scalable, observable, and secure Responsible AI features and infrastructure.

Partners with AI engineering leaders to improve organizational effectiveness, leadership capability, workforce readiness, and change management. Coaches leaders, advises on organizational design, talent, succession, culture, and high-stakes decisions. Uses HR data, diagnostics, talent processes, and AI insights to develop scalable people solutions, strengthen leadership teams, and support strategy execution.

Remotely operate and supervise robotic systems using teleoperation consoles, monitor video feeds and telemetry, execute real-time task adjustments, capture operational data and logs, identify anomalies, support data annotation and quality validation, and provide feedback to engineering for continuous improvement.

Remotely operate and supervise robotic systems via teleoperation consoles, monitor video and telemetry, perform real-time control and adjustments, collect and document operational data, identify anomalies, support data annotation, and provide feedback to engineering for continuous improvement.

The Senior Software Engineer will build AI solutions for clinical workflows, collaborating with teams to enhance software architecture and mentor other engineers.

The Research Engineer will develop AI algorithms for autonomous systems, collaborating with teams on end-to-end solutions and data-driven models.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.