Artificial Intelligence Jobs in Santa Clara, CA

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

Research and write technically rigorous content about AI, inference, compute, chips, systems, and infrastructure. Responsibilities include long-form analysis, product launches, leadership ghostwriting, customer case studies, technical briefs, social content, and data-driven visuals. The role partners closely with engineers and executives to develop original viewpoints, explain complex concepts, and establish Etched’s technical voice across audiences and channels.

Own product marketing for Veeam DataAI Privacy, including positioning, competitive differentiation, regulatory messaging, product launches, campaigns, sales enablement, analyst engagement, and public speaking. Translate privacy and AI regulations into customer and seller value, inform product and pricing decisions with market insights, and measure impact on pipeline and win rates. Partner closely with product, sales, marketing, executives, customers, and analysts.

Contribute to power estimation models for GPUs, explore architecture for energy efficiency, analyze performance versus power, and employ machine learning for modeling power and performance metrics.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

This role involves developing and maintaining CI/CD pipelines, enhancing deployment processes, ensuring infrastructure reliability, and promoting best practices within the AIDR platform team.

Leads complex government AI deployments from pre-sale discovery through production adoption, overseeing client relationships, delivery planning, cross-functional execution, change management, and executive communication. Translates agency workflows into technical requirements, manages risks and dependencies, drives measurable outcomes, and converts deployment feedback into product improvements. Builds deployment playbooks, operating cadences, and future delivery teams while supporting renewals and expansions. Requires strong AI/ML and enterprise systems fluency, senior stakeholder leadership, and 30–50% travel.

Designs and operates secure AI applications and backend microservices on AWS. Responsibilities include threat modeling, secure design reviews, vulnerability remediation, secure API development, AI guardrails, prompt testing, RAG pipelines, monitoring, audit logging, and incident readiness. The role collaborates with engineering, product, cloud infrastructure, and security teams to embed secure-by-design practices throughout the software development lifecycle.

As a Customer Success Manager, you will lead initiatives to enhance customer outcomes, manage accounts, and collaborate with engineers to optimize our SaaS platform.

As a Product Marketing Manager, you will lead sales enablement and go-to-market programs, manage product launches, support the revenue team, and collaborate on campaign design.

Leads the design, development, and deployment of scalable, reliable software and infrastructure solutions. Builds AI agents, automation workflows, observability integrations, and monitoring systems using technologies including C++, Python, Java, Claude, Codex, BigPanda, ITMP, and Prometheus. Collaborates across teams, drives engineering standards, mentors junior engineers, and manages complex end-to-end projects.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

As a Senior Data Scientist at Capital One, you will lead AI-powered product development using machine learning technologies, focusing on NLP and large language models to enhance customer experiences.

Lead large-scale SRE initiatives, define reliability and platform roadmaps, and build resilient distributed systems for AI-driven products. Design AI agents and skills for platform operations, improve automation and observability, and collaborate across cloud, security, platform, and AI/ML teams. Troubleshoot complex systems, drive incident and postmortem practices, and mentor engineers. The role requires deep expertise in systems architecture, networking, Kubernetes, public cloud, infrastructure as code, and automation.

The role involves architecting AI applications, delivering scalable solutions, conducting research, and implementing high-quality code for cloud platforms. Requires collaboration and effective communication.

Develops strategic partnerships and enterprise opportunities for Intel-based Physical AI and robotics solutions. Builds adoption strategies, business cases, go-to-market plans, account plans, and co-sell motions with cloud providers, systems integrators, software partners, robot OEMs, and AI model providers. Positions Intel technologies, represents market needs internally, influences product roadmaps, and drives ecosystem-led deployment scale across industries.

Lead positioning, messaging, and go-to-market for Zero Trust solutions addressing agentic AI. Develop market education, buyer personas, sales enablement, and partner with product to shape roadmap and customer narratives.

Manage the full eDiscovery lifecycle for litigation matters: scope and execute ESI collection, processing, review, and production. Use Relativity One, BrainSpace, iPro and other tools, ensure QC and metrics reporting, coordinate cross-functional stakeholders, mentor teams, and produce attorney-facing reports.

Leads engineering for an AI-native security operations product suite. Responsibilities include guiding architecture, setting engineering and product strategy, developing talent, engaging customers, and applying emerging AI innovations. The role requires leading high-velocity teams, shaping product roadmaps, coding with AI agents, building cloud software across AWS, GCP, and Azure, and collaborating with globally distributed teams.

Leads the architecture, strategy, and implementation of applied and agentic AI capabilities for HPE private and hybrid cloud offerings. Designs production-grade AI architectures, multi-agent systems, LLMOps, AI security, governance, observability, and knowledge layers. Builds reusable AI components, optimizes large-scale AI systems, guides technology selection, and converts proofs of concept into reliable services. Influences customers and internal teams through technical workshops, reference architectures, publications, and presentations while mentoring engineers and architects.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.