Artificial Intelligence Jobs in Santa Clara, CA

Develop and execute growth marketing for enterprise AI systems by translating technical work into compelling stories, videos, demos, and case studies. Shape company positioning, own social and content channels, build marketing tools and agentic workflows, track pipeline impact, and run experiments. The role combines strategic marketing, business storytelling, content creation, coding, and AI research.

Leads complex enterprise account projects, resolves high-impact issues, drives global process improvements, collaborates across teams to implement best practices, and monitors business performance metrics. The role requires at least eight years of relevant experience and a bachelor’s degree, or an equivalent combination of education and experience. It is based in a balanced hybrid work model with no stated travel requirement.

Leads PayPal’s horizontal enterprise AI portfolio from strategy and prioritization through architecture, delivery, adoption, profitability, and benefits tracking. Partners with business, technology, design, data science, and research teams to develop AI products, establish roadmaps, analyze customer needs, manage budgets, and drive execution across multiple enterprise business units.

Owns positioning, messaging, campaigns, and go-to-market coordination for NVIDIA’s Local AI platform. Translates technical capabilities, developer workflows, performance data, and customer outcomes into compelling stories and marketing assets for developers, enterprises, OEMs, partners, and AI enthusiasts. The role requires hands-on local AI building experience, strong generative AI and GPU knowledge, technical fluency, market analysis, community engagement, and cross-functional leadership.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

Leads product strategy, portfolio, commercialization, and organizational development for a global AI cloud infrastructure platform. Oversees GPU compute, bare metal, containers, networking, storage, AI training and inference capabilities. Owns roadmap, pricing, packaging, metering, SLAs, revenue, margins, utilization, and customer adoption. Partners across engineering, infrastructure, operations, sales, and solutions while building a high-performing product organization.

Manage NVIDIA’s global Lenovo OEM account and drive adoption and integration of NVIDIA accelerated computing platforms. Develop technical expertise across AI hardware, networking, software, and reference architectures; identify solution opportunities; package NVIDIA technologies into Lenovo offerings; and develop co-marketing and co-selling strategies. Coordinate product, engineering, supply planning, pricing, quality, and go-to-market teams while building relationships with senior stakeholders and supporting cloud and enterprise sales channels.

Develop and execute go-to-market strategy for AI products: define positioning and messaging, conduct market and competitive analysis, enable sales, coordinate launches, gather customer insights, and measure product performance.

Design and develop next-generation scale-up networking fabrics for AI data centers. Lead collaborative development across hardware, software, and applications to improve scalability, reliability, and robustness for large-model training. Create innovative concepts through production implementation, collaborate with cross-functional teams, and lead complex projects delivering large-scale networking and AI solutions.

Own new business and account growth across hyperscalers, neoclouds, enterprises, and frontier AI labs in the Americas. Manage complex technical sales cycles, layered OEM/ODM and channel routes, capacity commitments, design-ins, pipeline forecasting, contracts, and executive relationships. Translate semiconductor performance, power, and TCO advantages into commercial outcomes while partnering with product, engineering, marketing, legal, and finance. Build a qualified pipeline through an existing customer network and represent the company at industry events.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

Lead strategy, roadmap, and execution for Intel’s data center AI software portfolio. Define enterprise-ready generative AI, agentic, microservices, and turnkey solutions across cloud, hybrid, and on-premises environments. Analyze competitors, develop go-to-market positioning, and grow relationships with cloud providers, ISVs, neoclouds, and open-source communities. Partner with engineering, AI research, silicon architecture, documentation, and developer relations teams to deliver PRDs, deployment blueprints, APIs, SDKs, and validated software offerings.

Provides technical leadership for AI engineering and SDLC automation across a platform engineering organization. Designs and delivers AI agents, copilots, developer tools, and automated workflows integrated into CI/CD pipelines and engineering platforms. Establishes responsible AI governance, safety guardrails, evaluation methods, and secure integration patterns. Leads cross-functional initiatives, mentors engineers, drives adoption, and measures improvements in productivity, quality, reliability, and developer experience.

Lead architecture and implementation of AI-security products, define technical direction, drive cross-team initiatives, design secure scalable backend systems, and address systemic risks while remaining hands-on in coding, prototyping, and debugging.

This role involves developing and implementing testing frameworks for ensuring reliability and performance of AI security controls across platforms. The engineer will expand test coverage and contribute to quality metrics for AI models.

Leads SSD enablement, qualification, validation, and production readiness for NVIDIA, AMD, and other AI platforms. Coordinates customer and internal engineering teams to resolve hardware, firmware, PCIe/NVMe, thermal, power, performance, and interoperability issues. Identifies emerging AI storage workloads, conducts feasibility studies and proof-of-concepts, evaluates ecosystem trends, and develops future product opportunities.

Architects and delivers enterprise integration and API platforms using event-driven, asynchronous, cloud, and AI-augmented workflows. Develops reusable frameworks across SAP, SaaS, and partner ecosystems; governs APIs; integrates EDI, REST, GraphQL, and SOAP systems; and builds autonomous, self-monitoring and self-healing workflows. The role also requires programming, identity and access control expertise, CI/CD automation, observability, and close collaboration with Product and Engineering teams.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Manage major infrastructure projects in the energy sector focusing on electric transmission and substation upgrades, ensuring quality and stakeholder satisfaction.

Develop and maintain end-to-end LiDAR and camera perception software for robotics platforms. Responsibilities include sensor drivers, ROS 2 packages, GPU-accelerated processing, calibration, synchronization, recording and playback tools, Docker environments, packaging, CI/CD, hardware-in-the-loop testing, and production release automation. The engineer will support integration issues and collaborate closely with hardware, silicon, algorithm, manufacturing, and customer teams.

Serve as senior technical advisor to strategic partners for Arm-based Edge AI SoC solutions. Lead system-level architecture, IP selection, integration, optimization, technical engagements, demonstrations, and cross-functional resolution to drive design wins and product adoption while providing customer feedback to influence product roadmaps.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Lead technical strategy and architecture for CoreWeave's orchestration platform (SUNK). Own critical orchestration components, drive cross-org initiatives in scheduling, quota enforcement, and hyperscale scaling. Mentor senior engineers, establish reliability and observability best practices, and ensure workloads run reliably and efficiently for large GPU ML training and inference clusters.

Lead and scale teams building ServiceNow's AI evaluation platform. Define strategy and roadmap for benchmarks, automated scoring, verifier systems, and quality monitoring. Deliver unified internal and customer-facing evaluation services with APIs and dashboards. Drive novel evaluation methods, safety assessment, and cross-functional collaboration to ensure reliable, production-grade AI quality.