Artificial Intelligence Jobs in Santa Clara, CA

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.

Leads an SRE and platform engineering team responsible for NVIDIA’s enterprise-scale AI Platform Runtime and production services. Defines technical strategy and roadmaps, builds reliable distributed systems, drives AI-powered operational automation, and establishes SRE practices including SLOs, error budgets, observability, incident management, and disaster recovery. The role also involves cross-functional coordination, recruiting, coaching, career development, and leading teams through critical incidents and continuous reliability improvements.

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.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Lead the technical vision, architecture, roadmap, and implementation of Intel’s neuromorphic AI runtime stack. Own runtime performance, real-time behavior, production quality, compiler and framework interfaces, and firmware and driver execution contracts. Collaborate across compiler, hardware, application, customer, and partner teams to optimize workloads across silicon and simulators. Provide technical leadership, establish engineering practices, mentor engineers, and guide commercialization of event-driven AI technology for edge and robotic systems.

Leads an engineering team building scalable ML data infrastructure, including data ingestion, processing, storage, access, pipelines, and analytical platforms. Defines architecture, evaluates technologies, improves performance and reliability, translates data requirements into technical solutions, and ensures governance, security, and regulatory compliance. Provides mentorship, strategic roadmap input, and cross-functional collaboration with ML engineers, data scientists, platform engineers, and product teams.

Own reliability, automation, and observability across colocation facilities, on-premises GPU clusters, cloud environments, and customer-facing platforms. Provision and troubleshoot servers, networking, storage, Kubernetes, and hardware; build infrastructure-as-code and automation; manage capacity and cloud spend; create monitoring and alerting; support incident response, on-call operations, and root-cause analysis. Partner with hardware and software teams on CI/CD, QA, HPC workloads, and customer deployments.

Leads the design and hands-on development of production AI solutions, including multi-agent systems, self-improving evaluation loops, open-model integration, multimodal and voice pipelines, and full-stack applications. Engages strategic customers through discovery, workshops, prototypes, and production deployments. Sets technical direction, mentors solutions engineers, develops SambaStack tooling, publishes reference architectures, and communicates product feedback to engineering and product teams.

Leads strategic technical engagements for an AI inference platform from architecture evaluation through benchmarking, model enablement, deployment, and production rollout. Builds and manages a high-performing solutions engineering team, guides customer evaluations and deployments, translates customer requirements into product priorities, and develops scalable technical go-to-market assets. Requires deep expertise in AI inference, accelerator architecture, distributed systems, serving frameworks, performance optimization, and customer-facing technical leadership.

Build and ship AI-powered internal systems across investing, talent, founder services, finance, and operations. Projects include talent intelligence, sourcing and research tools, diligence workflows, firm knowledge search, operations automation, and reusable portfolio-company AI tools. The role requires end-to-end ownership, from identifying high-leverage problems and selecting the technical stack to building usable products, working with messy business data, and supporting portfolio founders with AI strategy and implementation.

Leads enterprise strategy, governance, and implementation of Agentic AI across semiconductor new product development. Builds AI-enabled design platforms, reusable engineering agents, knowledge repositories, and governance frameworks spanning analog, digital, verification, layout, testing, packaging, and systems engineering. Drives cross-functional transformation, establishes productivity and quality metrics, oversees AI lifecycle management, and delivers measurable improvements in development speed, engineering efficiency, IP reuse, and design quality.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Conduct fundamental research in generative AI, designing and implementing novel large-scale models for text, image, video, 3D, multimodal, or scientific applications. Publish original research, collaborate with research and product teams, present at conferences, and transfer innovations to product groups.

Lead development and optimization of local AI agent frameworks and runtimes for Windows PCs with NVIDIA GeForce RTX GPUs. Build secure, sandboxed, privacy-focused agent execution and inference systems, collaborate with AI research, driver, and open-source teams, and establish deployment guidelines. Mentor engineers while delivering production-ready code using Windows systems programming, C++, Python, LLM inference pipelines, GPU acceleration, and agent orchestration frameworks.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Builds developer and customer relationships across the Jetson ecosystem, guides partners through embedded Linux and Jetson software integration, promotes NVIDIA AI libraries and edge AI capabilities, supports production deployments, and communicates developer feedback to engineering and product teams to shape platform roadmaps, reference designs, positioning, and adoption.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Design and build production AI, data analytics, and cloud solutions for product engineering. Own end-to-end architecture, agentic systems, evaluation standards, data pipelines, AWS platforms, APIs, and user interfaces. Develop full-stack applications, establish technical standards, mentor engineers, manage developers, and partner with engineering teams to move prototypes into adopted production systems. The role requires hands-on Python, SQL, modern front-end, LLM orchestration, RAG, data engineering, cloud, and cost-aware architecture expertise.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Build and deploy production features for Marvell’s agentic AI software development lifecycle platform. Responsibilities include developing MCP integrations with engineering tools, automating CI/CD workflows, deploying containerized services on Kubernetes and AWS, writing production-quality code, measuring business impact, and collaborating with engineering and IT partners. The intern will work with LLMs, AI-assisted development tools, real users, and senior engineers.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Own the architecture and technical strategy for Square’s go-to-market technology stack. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish AI reliability and security guardrails, define evaluation metrics, and guide build-versus-buy decisions. Partner with Sales, Product, Data, and Engineering leaders while mentoring engineers and remaining hands-on across design, implementation, testing, deployment, and operations.

Lead AI product strategy and strategic operations across an enterprise software portfolio. Track revenue, customers, costs, metrics, and industry trends; support quarterly reviews and annual planning; conduct market sizing and buy-build-partner assessments; manage executive and cross-functional stakeholder alignment; and oversee AI initiatives that drive growth and operational efficiency.