Artificial Intelligence Jobs in Santa Clara, CA

Onsemi seeks non-engineering interns to support business operations, analytics, finance, supply chain, IT, HR, marketing, and other functions. Interns will analyze data, improve processes, develop reports and dashboards, assist with planning and operational initiatives, conduct research, and present recommendations. The role is fully on-site in Scottsdale, San Jose, or Allen and includes mentorship, professional development, and cross-functional collaboration.

Leads enterprise cloud infrastructure, data platforms, on-premises systems, AI enablement, and technology operations. Defines roadmaps, architecture standards, governance, and operational processes while managing technical teams and managers. Drives modernization, automation, platform reliability, security, resilience, cost optimization, and business continuity. Partners with executives, business stakeholders, cybersecurity, compliance, and risk teams to align technology investments with organizational goals and improve execution.

Owns the HR technology ecosystem, people data governance, analytics, workflows, integrations, and process automation across People Operations. The role administers HRIS and related platforms, improves employee lifecycle processes, develops dashboards and controls, supports payroll and benefits data flows, and leads HR technology workstreams for mergers and acquisitions. It partners cross-functionally to implement systems, resolve data issues, and apply AI to improve HR productivity.

Own and grow strategic global hyperscale accounts across cloud, AI, and data center customers. Develop global account strategies, build executive relationships, coordinate complex international sales opportunities, expand share of wallet, manage pricing and commercial agreements, and negotiate supply contracts. Partner with R&D, engineering, product, and procurement teams to secure specification wins and commercialize Brady solutions across customer value chains.

Conduct AI research for semiconductor manufacturing equipment, develop and evaluate solutions, translate research ideas into Python or C/C++ code, contribute to existing and new codebases, and share technical insights with the AI research team. Interns will complete meaningful project work, apply Lean principles, collaborate across cultures, and support the organization’s AI/ML research roadmap.

Process engineering intern supporting thin-film development in a research laboratory. Responsibilities include designing process optimization experiments, analyzing tool and metrology data, integrating plasma diagnostic signals, developing and validating reduced-order chamber models, simulating electromagnetic fields and plasmas, and performing supervised laboratory sample preparation and data collection. The role emphasizes Lean principles, continuous improvement, collaboration, and applying statistical, computational, and machine learning methods.

Leads a software engineering team building AI-native and cloud-native applications on the ServiceNow platform. Responsibilities include people management, hiring, coaching, technical strategy, architecture, delivery accountability, customer engagement, organizational leadership, and responsible AI adoption. The role drives team performance, product execution, AI/ML integration, platform evolution, engineering quality, incident response, and cross-functional alignment while developing engineers and future leaders.

Leads strategy, architecture, delivery, and support for enterprise business intelligence, big data, and AI/ML solutions. Manages complex cross-functional projects, budgets, testing, governance, service levels, system performance, upgrades, audits, and incident resolution. Oversees contingent workers and coordinates technology teams, vendors, and business stakeholders to deliver reliable analytics platforms and services.

Lead customer-facing design, deployment, and operation of agentic AI solutions on the ServiceNow platform. Analyze customer workflows, architect integrations and guardrails, deploy solutions to production, verify reliability and security, establish reusable technical standards, mentor engineers, advise executives, and feed field learnings back into product and engineering.

Develop and productionize GenAI, LLM, machine learning, and analytics solutions for ASIC semiconductor engineering. Build agentic systems, RAG workflows, predictive models, and failure-analysis tools; deploy and monitor AI solutions in cloud and enterprise environments; collaborate with architecture, design, data science, and IT teams; and communicate technical findings while identifying opportunities to improve engineering productivity, quality, cost, and cycle time.

Develop GenAI, machine learning, deep learning, and analytics solutions for ASIC semiconductor engineering workflows. Build LLM and agentic systems using prompting, RAG, Graph-RAG, benchmarking, and evaluation. Deploy and monitor production AI/ML solutions in cloud and enterprise environments, collaborate with architecture, design, data science, and IT teams, and communicate technical insights to diverse stakeholders.

Own an AI-native product spanning AI Specialists and Voice AI from customer problem definition through launch and production performance. Deploy solutions within enterprise environments, analyze agent execution logs and evaluation data, define quality standards, establish outcome metrics, lead design partnerships, translate customer insights into roadmap improvements, and coach product managers. The role requires shipping enterprise LLM products, designing evaluations, building prototypes, and generalizing forward-deployed solutions across customers.

Lead the design, development, deployment, and support of scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize AI training and inference for latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to technical strategy and mentoring stakeholders.

Lead the design, development, deployment, and support of foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Drive build-versus-buy decisions, optimize production AI systems for scalability, cost, latency, and throughput, and shape long-term technical roadmaps. Manage, mentor, and grow an engineering team while partnering with scientists, product managers, and technical program managers.

Build and support production AI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or mentor engineering teams.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI-powered products. Research state-of-the-art methods in language and vision-language modeling, optimize training and inference, develop platform or solution code, and translate research findings into customer-focused business outcomes.

Lead development of responsible, scalable AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize production AI systems for scalability, latency, throughput, hardware utilization, and cost. Partner with engineering, research, program management, and product teams while shaping the technical vision and roadmap for foundational AI platforms.

Designs and implements data warehousing, analytics, dashboards, semantic layers, and reporting solutions for supply chain planning operations. Collaborates with business, supply chain, and IT stakeholders to define requirements, document workflows, develop user stories, validate data, and improve SAP data accessibility through Databricks. Maintains data definitions and governance, tests data assets, ensures reporting accuracy, and supports self-service analytics. Requires deep supply chain experience, BI development expertise, and strong SQL, Python, and data modeling skills.

Conducts in-depth legal and business research for attorneys and administrative teams, synthesizes findings, advises on research strategies, manages research projects, and evaluates information resources. Develops training curricula, delivers onboarding, monitors legal-industry developments and emerging AI research technologies, maintains newsletters and intranet content, and recommends resource purchases. Requires expert knowledge of legal and business research platforms, strong communication skills, and the ability to manage competing priorities independently and collaboratively.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Design, develop, test, deploy, and support production AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI training and inference for scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while partnering with engineers, researchers, program managers, and product leaders. Mentor engineering teams and influence senior cross-functional stakeholders.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

Build and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to technical strategy and roadmap, collaborate cross-functionally, and lead or mentor engineering teams while applying emerging AI research in production.