Artificial Intelligence Jobs in Santa Clara, CA

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research LLM pretraining, fine-tuning, reinforcement learning, robustness, explainability, and behavioral models. Develop platform or solution-level code, reproduce research, document experiments, and contribute to technical reports or conference submissions.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads architecture and design reviews, establishes AI engineering standards, drives technical roadmaps, and mentors senior AI engineers.

Design, develop, optimize, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization of production AI systems for scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, establish governance standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineering staff.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Owns complex AI architecture, defines reliability objectives, leads technical reviews, ensures security and compliance, contributes to long-term technical roadmaps, and mentors senior and principal engineers.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable machine learning solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, engineers, and product managers while independently pursuing impactful research agendas. The role emphasizes deep learning, NLP, model fine-tuning, robustness, explainability, and deployment at scale.

Design, develop, deploy, and support foundational AI systems, including LLM training and inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define AI service-level objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable AI solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, software engineers, ML engineers, and product managers while pursuing impactful research and mentoring junior team members.

Design, develop, deploy, and support foundational AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize distributed AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering teams.

Build and lead scalable generative AI platforms, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization; architect multi-model orchestration pipelines; establish technical standards and governance; and mentor senior AI engineers while shaping the long-term platform roadmap.

Build and optimize foundational AI systems, including LLM training and inference, agentic workflows, vector search, guardrails, evaluation, and observability. Partner with engineering, research, product, and program teams to deliver scalable AI products. Lead model and infrastructure optimization for cost, latency, throughput, and hardware utilization; establish governance and engineering standards; contribute to technical roadmaps; and mentor senior AI leaders.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agent workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Build training and deployment pipelines, improve datasets and evaluation methods, support responsible AI practices, and mentor junior engineers while contributing to long-term technical strategy.

Designs, develops, deploys, and supports scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes model performance, GPU/TPU utilization, latency, throughput, and cost; owns complex AI architecture; defines reliability objectives; leads technical reviews; and mentors engineering teams.

Conduct applied research in AI foundations, large language models, and agentic AI. Design, train, evaluate, validate, and deploy foundation models at scale using modern deep learning frameworks and cloud infrastructure. Develop novel methods, deliver platform or product code, collaborate with data scientists, engineers, and product managers, and communicate findings through technical reports or conference publications.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across production systems. Own complex AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews, collaborate across engineering and product teams, and mentor senior and principal associates.

Design, develop, deploy, optimize, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, design reviews, and technical roadmaps for scalable production AI. Optimize model performance, hardware utilization, latency, throughput, and cost while ensuring responsible AI standards. Mentor senior technical staff and collaborate with engineers, researchers, program managers, and product managers.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance governance for scalable AI platforms, establish engineering standards, contribute to technical roadmaps, and mentor senior AI engineers. Collaborate with scientists, engineers, technical program managers, and product managers to deliver responsible AI products.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models across training, validation, optimization, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products, translate research into business outcomes, and publish technical reports or conference papers. The role requires expertise in deep learning, model optimization, self-supervised learning, robustness, explainability, or RLHF.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and multi-model orchestration. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform strategy. Apply state-of-the-art optimization techniques to improve model scalability, latency, throughput, hardware utilization, and cost. Mentor senior technical leaders and collaborate with engineers, scientists, product managers, and program managers.

Design, develop, deploy, optimize, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization across scalable AI platforms, establish engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineers.

Advise Tenstorrent during its growth phase on technology transactions and a broad range of legal matters, including intellectual property, sales, marketing, patents, trademarks, standards, business development, M&A, recruiting, and procurement. The role requires building legal processes, partnering with cross-functional teams, and supporting a regulated AI technology business.

Develop customer-specific AI solutions for consequential business decisions. Investigate internal and public information, evaluate evidence, identify uncertainty, and design reliable AI workflows. Work directly with customers to understand needs, prototype solutions, test them with users, and improve their usefulness. Translate successful workflows into repeatable product capabilities while partnering with engineers on integrations and missing technical functionality.

Supports product strategy, requirements, and roadmaps for AI-powered features. Partners with engineering and design teams to translate customer insights into actionable requirements and data-backed development plans. The internship includes product research, competitive analysis, strategic planning, roadmap development, cross-functional collaboration, and exposure to product management methodologies.

Build and maintain scalable AI Search software across the full development lifecycle, including Java and front-end development, ServiceNow administrative tools, search UI components, GraphQL APIs, analytics dashboards, and machine-learning functionality. Own design, coding, testing, deployment, reliability, and performance optimization. Develop automated and manual tests using Jenkins, JUnit, Playwright, Selenium, and related frameworks. Collaborate with Product Owners and engineering teams in Agile Scrum processes while supporting customer-reported search UI and UX issues.

Provides technical leadership for scalable cloud, data, and AI foundations supporting mapping databases and software-defined vehicle experiences. Designs data platforms, pipelines, APIs, governance, observability, and cloud-native services; productionizes agentic AI workflows using enterprise knowledge sources; establishes reusable engineering patterns, security, reliability, and deployment standards; leads architecture reviews and cross-team workshops; and mentors engineers. The role requires hands-on work across distributed systems, infrastructure, data engineering, cloud platforms, and responsible AI enablement.

Lead complex global insider-risk investigations involving intellectual property theft, data exfiltration, espionage, fraud, and workplace violence threats. Develop investigative plans, conduct interviews, coordinate with law enforcement and intelligence partners, manage cases across jurisdictions, and prepare executive reports. Identify systemic security weaknesses, recommend mitigations, and develop employee awareness programs. The role requires expertise in investigations, insider-threat analysis, sensitive information handling, data analysis, cybersecurity, and responsible AI-enabled workflows.