Artificial Intelligence Jobs in San Jose, CA

Reviews and scores robotics data episodes using standardized quality rubrics. Identifies failures, unsafe behavior, edge cases, and recurring quality trends; documents findings and provides actionable feedback. Collaborates with stakeholders and teleoperators to improve review workflows, standards, training, and documentation while meeting throughput and accuracy targets.

The Senior Lead AI Engineer will develop and support AI software components, lead cross-functional teams, and optimize AI solutions for Capital One's banking systems.

Lead AI Engineer responsible for developing AI software components, partnering with cross-functional teams to deliver AI-driven products, and optimizing large-scale AI systems.

The Senior Lead AI Engineer will develop AI software components, partner with cross-functional teams, and contribute to AI system optimization. Key responsibilities include model training, deployment, and leveraging AI technologies to enhance banking services.

The Lead AI Engineer will design, develop, and support AI software components, optimizing AI systems for performance and scalability while collaborating with cross-functional teams.

Lead a team of data scientists to develop AI/ML solutions, leveraging advanced technologies for customer interactions and product innovations. Build and implement NLP models for large-scale applications while collaborating with cross-functional teams.

The Senior Lead AI Engineer will develop, test, and support AI software, design new models, and enhance AI product offerings at Capital One while collaborating across teams.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

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.

Leads product positioning, messaging, go-to-market strategy, launches, segment campaigns, technical content, competitive differentiation, and global sales enablement for FlashBlade//EXA scalable AI and HPC storage solutions. Partners with Product Management, Sales, and Corporate Marketing to drive adoption, revenue growth, and market leadership across enterprise, life sciences, financial services, and academic customers.

Build and deploy full-stack AI/ML and agentic applications for strategic enterprise customers. Partner with technical and executive stakeholders to move use cases from prototypes to production, design scalable AI architectures, establish reusable solution patterns, and advise on AI roadmaps, governance, deployment, and operations. Mentor internal teams, support enablement, and provide product feedback. The role combines remote and customer-facing engagement with occasional travel up to 20%.

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.

Own the strategy, architecture, evaluation, and production delivery of Jerry.ai’s AI agents and workflow automation platform. Lead chatbot, voicebot, computer-use agent, document validation, transaction orchestration, and external system automation initiatives. Evaluate emerging AI technologies, make technical investment decisions, partner with OpenAI, and collaborate closely with engineering and prompt engineering teams. The role requires hands-on experience launching LLM systems and managing evaluations, regressions, latency, cost, and failure handling.

Own the strategy, architecture, evaluation, and production deployment of Jerry.ai’s next-generation AI agents and workflow automation platform. Lead conversational agents, voicebots, computer-use agents, document validation, transaction orchestration, and external insurance-system integrations. Evaluate emerging AI technologies, make technical investment decisions, partner with engineering and OpenAI, and ensure systems meet requirements for reliability, latency, cost, regression prevention, and failure handling.

Conduct applied research to design, train, evaluate, and deploy AI foundation models and recommendation/personalization systems. Collaborate with cross-functional teams to build models at scale using PyTorch, cloud infrastructure, and open-source tools. Publish research, deliver platform-level code, and drive long-term research agendas to improve customer experiences.

Design and build NVIDIA’s enterprise AI agent platform, including agent blueprints, runtime safety and policy enforcement, multi-agent orchestration, credential brokering, checkpointing, observability, evaluation, and self-improvement loops. Deploy reliable long-running services at scale, integrate enterprise systems, support secure execution, and participate in on-call operations. The role requires extensive distributed systems or infrastructure experience and hands-on agent development.

Designs, develops, tests, and supports ServiceNow automation for ITSM, infrastructure, cloud, endpoint, identity, monitoring, and security operations. Builds workflows, integrations, scripts, catalog automations, and AI-enabled solutions using APIs, IntegrationHub, and MID Server. Integrates ServiceNow with enterprise platforms, improves reliability and operational reporting, maintains CMDB alignment, documents solutions, troubleshoots production issues, and ensures compliance with architecture, security, governance, and release standards.

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.

Develop, optimize, and ship video processing, generative video, and 3D reconstruction algorithms (e.g., NeRF, 3D Gaussian Splatting) across desktop and mobile. Integrate and deploy deep learning models, run research and evaluations, build production-ready modules, and create test environments and tools for runtime verification of video and 3D pipelines.

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.