Artificial Intelligence Jobs in Santa Clara, CA

Leads Data Quality Analysts supporting AI model evaluation and improvement. Owns quality across workflows through audits, calibrations, feedback loops, trend analysis, and stakeholder alignment. Translates evolving guidelines into team direction, manages operational performance, supports hiring, drives process and tooling improvements, handles complex decisions, and coaches team members. The role balances quality, delivery, resource management, and continuous improvement in a human-in-the-loop AI environment.

Leads the engineering organization responsible for F5’s OpenTelemetry-based AI Data Fabric. Owns the platform’s strategy, roadmap, architecture, reliability, security, privacy, cost efficiency, vendor relationships, and multi-cloud expansion. Builds and develops engineering managers, drives predictable delivery, establishes SLOs, oversees incident response, and ensures data quality and platform contracts support AI/ML, analytics, data engineering, and catalog teams.

Lead the multi-generational roadmap, product definition, business cases, competitive strategy, customer sampling, and lifecycle management for NXP’s edge NPU hardware portfolio. Define silicon specifications, chiplet architectures, SoC integration requirements, and make-versus-buy decisions while coordinating architecture, engineering, software, sales, marketing, customers, and executives. Drive design wins, demonstrations, product ramp, sustaining activities, and end-of-life transitions across AI inference hardware generations.

Owns the product strategy and roadmap for NXP’s edge NPU software stack, including compilers, runtimes, SDKs, developer APIs, model optimization, eIQ integration, and developer experience. The role drives AI framework and open-source partnerships, model support, benchmarking, beta programs, software lifecycle policies, GTM enablement, and system-level demonstrations. It requires strong AI product management experience, compiler and inference pipeline expertise, ecosystem strategy, and familiarity with edge accelerator software.

Engage university researchers and laboratories to accelerate adoption of NVIDIA AI and computing platforms. Advise research teams, integrate NVIDIA frameworks into scientific workloads, identify ecosystem opportunities, monitor research trends, coordinate conferences, and translate researcher feedback into product roadmaps and enablement programs. The role requires deep AI and computational science expertise, research credibility, hands-on experience with modern AI systems, and strong relationships across academic and open-source research communities.

Leads strategic UX research for AI and developer platforms, studying tools, APIs, runtimes, documentation, workflows, and agent-mediated experiences. The researcher identifies high-value questions, applies qualitative and quantitative methods, influences product and platform direction, and translates insights into design principles and recommendations. Responsibilities include engaging developers, customers, and partners; representing user needs in leadership and roadmap discussions; and mentoring peers to improve evidence-based research practices.

Supports AI agent and Claude skill development for IT and security workflows, including MCP connector integration, prompt engineering, testing, and documentation. Assists with security operations such as log review, alert triage, and incident documentation. Provides IT operations and helpdesk support involving hardware procurement, onboarding and offboarding, Jamf device management, SaaS access requests, and Confluence knowledge-base maintenance.

Develop GPU-accelerated techniques for generative AI training and inference, optimize algorithms and performance across NVIDIA CPU and GPU architectures, contribute to frameworks, libraries, and open-source projects, and collaborate with developers, researchers, and hardware and software engineering teams. The role focuses on LLMs, diffusion models, multimodal systems, low-level optimization, and communicating innovations through technical publications and industry conferences.

Design and implement backend orchestration for agent workflows and runtime in microservices. Build scalable LLM-powered systems (RAG, tool-use, multi-step reasoning), integrate LLM/TTS/ASR, develop agents with frameworks like LangChain/Dify/OpenAI SDK, optimize prompts, run integration tests, and collaborate with research and product teams to deploy agentic AI across verticals.

Lead mechanical architecture and design for hyperscale AI networking hardware, including electronic packaging, PCB and system interfaces, cooling, and assemblies. Develop CAD models, drawings, and tolerance analyses; guide cross-functional technical decisions; verify designs through prototyping and testing; investigate failures; and support suppliers and manufacturing through production introduction.

Serve as a Microsoft Dynamics 365 functional lead delivering CRM/ERP assessments, design, configuration, fit-gap analysis, data migration, testing, training, and client-facing implementation support. Lead project tasks, create functional requirements and process flows, support proposals and business development, and coach junior staff to deliver solutions leveraging Microsoft platforms.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead large Microsoft Dynamics 365 (CRM/CE) implementations as a Senior Manager: engage senior clients, design functional solutions, oversee delivery lifecycle, coach teams, refine processes, and ensure operational excellence and adherence to firm standards.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Lead the design and development of high-performance AI networking software for NVIDIA BlueField DPU and ConnectX products. Manage hyperscaler technical engagements involving coding, system bring-up, performance tuning, failure analysis, and production hardening. Collaborate across engineering, product, architecture, and customer teams to translate needs into product features and roadmap improvements. Drive reliability, performance, debuggability, and tooling enhancements across firmware, drivers, operating systems, and applications.

Provide high-judgment executive support to three leaders, including two co-founders. Manage calendars, communications, travel, logistics, expenses, relationship tracking, meeting preparation, and confidential information. Improve leadership operating systems, information flow, decision follow-through, and selected operational projects. The role requires exceptional prioritization, discretion, anticipation, organization, and the ability to use AI to increase leverage while supporting a frontier science and engineering organization.

Leads portfolio marketing and solutions strategy for Itron’s Grid Edge Intelligence offerings. Develops positioning, messaging, value propositions, integrated solutions, market narratives, and go-to-market initiatives for electric utilities. Partners with product, sales, customer success, and corporate marketing teams to support solution selling, customer engagement, competitive differentiation, and commercial growth. Represents the portfolio with customers, analysts, partners, and industry stakeholders while monitoring performance metrics and utility technology trends.

Leads Cisco’s applied AI research organization, setting long-term research strategy across networking, security, programming, and human-computer interaction. Directs research teams, advances representation learning, structured prediction, generative models, and large language models, and translates prototypes into production enterprise solutions. Partners with product, engineering, and design teams, mentors researchers, and establishes ethical AI, privacy, security, and governance standards. Represents Cisco through publications, conferences, and external partnerships.

Lead legal counsel for AI and machine learning matters, advising engineering and product teams on intellectual property, regulatory compliance, contracts, privacy, open-source licensing, product design, and risk mitigation. Develop AI/ML legal playbooks, compliance processes, training, and IP protection strategies while collaborating with cross-functional stakeholders and presenting emerging risks to executives and the Board.

Defines enterprise AI and machine learning architectures spanning Generative AI, agentic AI, data, applications, cloud, infrastructure, and enterprise systems. Translates business requirements into architecture blueprints, technical strategies, and implementation roadmaps; develops reusable standards and design patterns; evaluates technology alternatives; and leads technical reviews. The role also establishes patterns for MLOps, model serving, monitoring, governance, security, and deployment across cloud, hybrid, on-premises, and restricted environments while mentoring engineering teams.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

Develop large-scale AI cloud and infrastructure platforms across cloud services, Kubernetes, distributed training, model serving, AI agents, MLOps, security, and high-performance systems. New graduates may work in cloud platform engineering, SRE, AI operations, model serving, AI application engineering, security engineering, or AI research, with opportunities to rotate across areas.

Own the full QA lifecycle for AI-powered legal solutions, including unit, integration, system, regression, performance, scalability, and security testing. Develop edge-case and boundary tests, validate AI outputs, integrate automated test suites into Azure DevOps CI/CD pipelines, track defects and quality metrics, and collaborate with analysts, engineers, security teams, and QA leadership to ensure reliable, production-ready solutions.

Evaluate and implement AI tools, agent harnesses, and models for software engineering workflows. Build evaluation pipelines and reference workflows, optimize distributed systems for performance and cost, collaborate with vendors, guide tool selection, and train engineering teams. The role requires strong Python software engineering, LLM and agentic AI expertise, CI/CD knowledge, distributed systems experience, and the ability to translate evaluations into practical recommendations.

Build AI tools, agent workflows, APIs, automation systems, and evaluation capabilities for robotic engineering and research. Automate model training, experiment setup, testing, simulation, and real-robot evaluation. Develop logging, analysis, debugging, and data-generation tools while applying statistical rigor to experiments. Ensure solutions are reliable, secure, safe, measurable, and integrated into team operations. The role requires hands-on robotics experience, production Python, C++ familiarity, Linux, CI/CD, Docker, APIs, and distributed systems.