Artificial Intelligence Jobs in Sunnyvale, CA

The Technical Solutions Engineer resolves customer issues, troubleshoots complex network environments, and collaborates with engineering teams to ensure customer satisfaction.

The Solutions Architect will implement ERP projects focusing on NetSuite integration, optimize functionalities, and ensure system reliability while collaborating with stakeholders.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Design and deploy scalable AI inference architectures for strategic partners. Build and operate GPU inference recipes, optimize pipelines using leading inference backends, and guide disaggregated serving deployments. Evangelize Kubernetes, compute fabric, DevOps, and observability practices while mentoring customers and internal teams. Lead technical engagements, resolve complex infrastructure issues, create reference architectures, and provide product insights. The role also involves agent design, sandboxing, memory and retrieval systems, evaluation, skill governance, and model customization.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Apply deep battery-systems expertise to define SOC/SOH/thermal states and BMS logic, incorporate safety constraints, design agentic AI decision frameworks, analyze large-scale vehicle and telematics data with Python, and collaborate across BMS, data science, and AI teams to develop adaptive battery management strategies.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Lead the Solutions Architecture team, managing complex implementations of AI solutions, mentoring technical staff, and aligning customer needs with product roadmaps.

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.

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.

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.

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.

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 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.

As a Senior Hardware Systems Engineer, you will lead AI rack and cluster infrastructure designs, manage deployments, and collaborate with cross-functional teams to optimize data center solutions.

As a Senior Formal Verification Engineer, you will develop verification strategies and environments for AI SoCs, ensuring the correctness of digital designs through formal verification methods and collaboration with engineering teams.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.