Artificial Intelligence Jobs in Santa Clara, CA

Automate and streamline AI operations by building solutions that reduce repetitive tasks, accelerate incident response, and maintain resilient, scalable AI production environments. The intern will work with scripting, systems administration, monitoring, deployment automation, cloud platforms, Kubernetes, CI/CD, and AI/ML model deployment and monitoring.

Build data pipelines and machine learning workflows for Cisco products. Develop, train, deploy, and scale models using Python and ML frameworks such as PyTorch or scikit-learn. Work with distributed processing and orchestration tools including Spark and Kubernetes while supporting performant, reliable AI data systems. The 12-week internship includes technical learning, cross-functional collaboration, networking, and exposure to product and platform development.

Leads and mentors a backend engineering team building cloud-native provisioning and orchestration services for CrowdStrike’s AI Detection and Response platform. Owns technical vision, architecture, roadmap, reliability, incident response, engineering practices, hiring, and team development. This player-coach role includes approximately 20–30% hands-on coding, collaboration with product and engineering stakeholders, and delivery of scalable distributed systems supporting large-scale security event processing.

Leads customer deployments of integrated hardware and software systems across government and industrial organizations. Responsibilities include deployment planning, customer relationship management, technical proposals, risk mitigation, milestone tracking, roadmapping, demonstrations, presales support, and business development. The role bridges customer stakeholders with engineering and commercial teams while ensuring proposed solutions are successfully delivered.

Own CoreWeave’s AI training infrastructure narrative, positioning, competitive messaging, customer evidence strategy, go-to-market approach, and sales enablement. Translate Kubernetes, scheduling, bare-metal performance, and distributed training capabilities into compelling messaging for AI research leaders, platform engineers, developers, and technical architects. Partner with technical teams on benchmarking, collaborate with Sales, Solutions Architects, and DevRel, and measure impact through deal velocity, contract value, competitive win rates, adoption, and retention.

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.

Developers Relations technical practitioner who helps enterprise teams deploy production AI workloads on Lambda. Creates technical guides, demonstrations, open-source examples, talks, workshops, and videos; grows reach through professional networks, communities, customers, and partners; represents Lambda at conferences and podcasts; gathers field feedback; and collaborates with engineering, MLE, marketing, and go-to-market teams.

Own the financial services product line from discovery through launch and iteration. Set the roadmap, determine which deployment capabilities become core product features, conduct user discovery, define quality standards, and establish accuracy, human-review, citation, and safe-failure models for regulated workflows. Prototype in design or code, collaborate with engineers and designers, understand the market, and deliver reusable AI capabilities across financial institutions.

Leads hands-on design, development, and optimization of AI data movement and distributed storage systems. Responsibilities include integrating NVIDIA NIXL and DDN Infinia with GPU inference platforms, optimizing GPU-to-storage I/O using GPUDirect Storage, RDMA, and NVMe-over-Fabrics, developing KV cache and multi-tier storage strategies, benchmarking production systems, resolving performance bottlenecks, influencing distributed inference architecture, and mentoring engineers.

Develop and support AI-powered applications, automation solutions, and agent-based systems for business operations. Responsibilities include software development, integrations, Azure AI configuration, RAG and prompt engineering, testing, architecture participation, monitoring, troubleshooting, documentation, security collaboration, code reviews, and user enablement. The role works cross-functionally with engineers, stakeholders, legal teams, data professionals, and technology partners to deliver scalable and reliable AI solutions.

Create and distribute technically accurate resources about deploying production AI workloads on Lambda. Build enterprise AI practitioner reach through technical content, talks, workshops, videos, conferences, podcasts, communities, and partnerships. Collaborate with machine learning and engineering teams on guides, demonstrations, and open-source examples. Gather enterprise deployment feedback and share insights with technical and go-to-market teams to shape Developer Relations priorities and product direction.

Leads NVIDIA’s embedded GPU and long-life board product portfolio, managing customer relationships, design wins, pricing, supply commitments, lifecycle planning, and product roadmaps. The role coordinates Engineering, Sales, Finance, Operations, campaign teams, and partners through product introduction, launch, long-life support, and end-of-life transitions. It requires advanced hardware and software platform experience, commercial acumen, lifecycle management expertise, strong communication, and at least 25% travel.

Drive business development and FPGA design wins across robotics, autonomous systems, industrial automation, and edge AI. Build OEM, vendor, integrator, and ecosystem partnerships; position FPGA solutions against competing architectures; support technical sales, customer engagements, sales enablement, opportunity qualification, and deal progression. Provide market feedback to product and engineering teams while developing solution architectures, competitive positioning, ROI analyses, and customer-driven growth strategies.

Develop and promote NVIDIA world models for autonomous vehicles through technical content, benchmarks, demos, workshops, developer engagement, and partner enablement. Design and post-train foundation models, prototype generative AI systems, explain technical concepts to business and developer audiences, and collaborate with research, software, product, sales, and customer teams. Serve as an autonomous-vehicle world-models expert while advancing thought leadership, adoption, and deployment of NVIDIA AI platforms.

Own FloQast’s multi-region AI platform infrastructure across AWS, including Bedrock model runtimes, sandboxed code execution, Terraform, observability, cost controls, CI/CD, security, and reliability. Support Transform, AI Matching, and AutoBuilder by managing scaling, throttling, tenant isolation, journey-based SLOs, model and prompt delivery gates, and AI-specific on-call processes across US, EU, and AU regions.

A 12-week internship focused on building data pipelines, machine learning workflows, and distributed systems. The intern will use Python, ML frameworks, Spark, Docker, and Kubernetes to develop, train, deploy, and scale models and intelligent data solutions. Responsibilities include supporting reliable, performant AI products and gaining experience with cloud platforms, scalability, fault tolerance, and production ML deployment.

Leads GlobalFoundries’ Edge and AI Compute end-market strategy across MCU, MPU/NPU, FPGA, embedded vision, robotics, and connected-device applications. Builds market models, customer opportunity maps, competitive assessments, and technology requirements; supports design wins and customer engagements; develops market collateral and requirements documents; and partners with Sales, Product Management, Technology Development, Design Enablement, and executives to drive roadmap decisions, funnel growth, and revenue.

Leads Finance-wide AI and data transformation, including governance, semantic models, AI agent standards, initiative prioritization, adoption, and value realization. Builds and manages a central Finance AI and Data Governance team while partnering with Finance, IT, Enterprise AI and Data, and Security. Establishes controls, quality standards, operating models, and scalable capabilities that improve planning, reporting, forecasting, productivity, decision support, and risk management.

Engage leading academic AI labs researching foundation models, LLMs, multimodal systems, reasoning, training, inference, and scalable AI infrastructure. Advise researchers on NVIDIA platforms, identify impactful workloads, track frontier research, translate academic feedback into product and platform opportunities, and support technical workshops, university programs, and research conferences. The role requires deep foundational AI expertise, hands-on experience with AI training or inference, and strong research engagement credibility.

Lead a team to deliver AI powered products, leveraging machine learning and NLP for customer applications, while collaborating with cross-functional teams.

The role involves leading a team to develop AI and ML solutions, manage customer interactions, and analyze large data sets using various technologies.

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

Leads Altera’s AI transformation of hardware design by defining AI/ML strategy, deploying tools across design, verification, and implementation workflows, building data and evaluation foundations, and measuring productivity impact. The role requires deep semiconductor and EDA expertise, hands-on ML/LLM experience, cross-functional influence, vendor evaluation, stakeholder engagement, and mentoring without direct reports.

Defines the technical vision and architecture for NVIDIA’s next-generation autonomous driving stack, integrating classical safety systems with learning-based autonomy. Leads engineering teams across prediction, planning, control, decision-making, and safety; oversees productionization of end-to-end driving pipelines and foundation models; and partners with research teams to bring robotics, embodied AI, and generative AI advances into scalable autonomous vehicle systems.

Advise Product, Engineering, Design, Marketing, and business teams on privacy, data protection, accessibility, consumer protection, copyright, contracts, IP, and emerging AI and autonomy technologies. Lead compliance, safety, patenting, invention harvesting, product integration reviews, and legal process development. Translate legal guidance into actionable product workflows, manage complex cross-functional projects, monitor regulatory developments, review external-facing documents, and support M&A transactions.