Artificial Intelligence Jobs in Santa Clara, CA

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.

Own and grow a strategic global OEM account by executing account plans, building senior customer relationships, managing multi-product pipelines, forecasting accurately, achieving revenue and margin goals, identifying strategic opportunities, and representing customer needs internally. The role requires cross-functional influence, global account management, technical sales expertise, CRM discipline, and proficiency with Microsoft Office and Salesforce.

Optimize LLM inference on Intel GPUs by profiling performance bottlenecks, developing custom kernels, and improving attention, MoE, quantization, and operator fusion. Contribute optimizations to vLLM, SGLang, and PyTorch, perform roofline analysis, support scale-out inference, and collaborate with architecture and compiler teams to influence future GPU roadmaps.

Leads NVIDIA’s US Medtech Account Managers and drives enterprise sales growth across named manufacturing accounts. Responsibilities include demand generation, forecasting, executive relationship development, channel and partner collaboration, revenue target attainment, and coordination with internal teams to address customer needs. The role focuses on selling NVIDIA hardware and software for AI and accelerated computing in healthcare and medtech markets.

Leads product marketing for Cisco’s AI infrastructure, compute, virtualization, HCI, and software ecosystem solutions. Develops joint GTM strategies with partners such as Nutanix and Red Hat, creates technical marketing assets, enables sales and channel teams, manages product launches and campaigns, and represents solutions at conferences, briefings, webinars, and partner events.

Own F5’s enterprise customer experience AI strategy, roadmap, governance, and delivery. Re-architect customer journeys using generative and agentic AI, establish standards for evaluation, security, privacy, oversight, and responsible deployment, and make build-versus-buy decisions. Prototype solutions, define technical requirements, drive adoption, measure operational and customer outcomes, and present recommendations to executives. The role spans go-to-market, service, self-service, renewal, partner, and internal CX workflows.

Design, architect, deploy, and optimize advanced machine learning models for real-time onboard autonomous driving. Lead end-to-end ML projects, improve neural architectures and inference performance, ensure robustness and safety in critical systems, mentor engineers, collaborate across engineering teams, and influence technical roadmaps. The role focuses on foundation models, computer vision, sensor-to-action learning, and onboard autonomy.

Architect and lead production AI products, including chatbots, document processing systems, and agentic workflows. Build FloQast’s centralized AI platform with model routing, vector search, provider management, observability, and MCP integrations. Integrate AI systems with accounting platforms, repositories, APIs, and cloud environments. Establish AI engineering standards, ensure compliance and reliability, and mentor engineers while providing technical leadership across the organization.

Own and extend NVIDIA’s production linear programming supply chain optimization model. Formulate constraints and objectives, analyze dual variables and sensitivities, maintain data integrity, and translate semiconductor supply-chain realities into mathematical models. Partner with procurement, operations, engineering, and leadership to evaluate constraints, scenarios, sourcing, capacity, and planning decisions. Evolve the optimization architecture and explore AI, machine learning, GPU acceleration, and cuOpt enhancements.

Build the developer ecosystem for NVIDIA Dynamo, an open-source distributed AI inference framework. Responsibilities include creating demos, sample code, documentation, videos, technical talks, workshops, and office hours; collaborating with inference projects and open-source maintainers; troubleshooting deployments; gathering developer feedback; influencing engineering and product priorities; and communicating progress externally. The role requires hands-on experience with model serving, Kubernetes, distributed inference, performance optimization, and developer or partner programs.

Provide advanced technical support for NVIDIA Ethernet switching and AI infrastructure products. Troubleshoot Linux, server, hardware, operating system, networking, installation, interoperability, and operational issues for enterprise customers. Analyze protocols and packet captures, lead critical issue resolution, collaborate with Engineering, Marketing, and Support, and develop support methodologies and documentation. The role focuses on data center, HPC, networking, and AI infrastructure environments.

Own and evolve the marketing technology ecosystem across Marketo, Salesforce, 6sense, Qualified, and AI platforms. Build integrations, workflows, automations, and AI-powered capabilities; improve lead and account processes; prototype and scale new technologies; and define the MarTech roadmap. Partner with Marketing, Sales, Revenue Operations, IT, and Data teams to deliver scalable solutions that accelerate campaigns, reduce manual work, improve funnel performance, and generate pipeline.

Design, build, deploy, and operate production-grade AI applications, LLM solutions, and enterprise agents. Develop RAG pipelines, integrations, evaluation frameworks, secure connectors, and reusable AI platform components. Own CI/CD, observability, monitoring, model and prompt versioning, security controls, and continuous improvement. Partner with business and technology teams to deliver measurable outcomes, mentor engineers, and apply responsible AI practices across enterprise workflows.

Develop production AI-powered systems that improve software quality, engineering productivity, debugging, and developer workflows. Build intelligent services involving code analysis, retrieval, orchestration, evaluation, monitoring, and large-scale engineering data. Partner across teams to translate workflow challenges into reliable AI solutions, evaluate models and frameworks, and optimize quality, latency, cost, and reliability from concept through deployment and continuous improvement.

Leads public relations and communications for Cisco’s AI software and platform products. Owns launch positioning, messaging, media relations, thought leadership, partner and customer storytelling, executive counsel, and cross-functional communications campaigns. Builds relationships with technology, business, security, and AI media; translates product strategy into compelling narratives; manages agencies, vendors, timelines, measurement, and global collaboration while responding rapidly to market and news-cycle opportunities.

Set technical direction for an AI-native software engineering team building security workflow tools. Lead complex architectural decisions, guide engineers, review designs and code, and collaborate with technical leaders across teams. The role requires extensive hands-on software engineering expertise, strong architectural judgment, mentoring ability, and experience making decisions for scalable systems. Preferred experience includes security tooling, AI agent APIs, MCP servers, and AI-native engineering practices.

Debug, profile, and optimize AI cluster networking and GPU performance across Linux kernels, PCIe, SmartNICs, RDMA/RoCEv2, NCCL, and application communication. Investigate boot, driver, latency, throughput, congestion, and hardware trace issues; analyze packets, logs, profiles, and observability data; and collaborate with hardware and software teams to remove end-to-end bottlenecks.

Own the architecture and technical direction of a heterogeneous AI SoC runtime spanning sensor ingest, preprocessing, inference, postprocessing, and application delivery. Define execution, memory, synchronization, driver, firmware, compiler-runtime, observability, resilience, and validation architectures. Lead cross-functional engineers while contributing hands-on to production systems software, performance optimization, APIs, and low-level interfaces for robotics and edge AI workloads.

Own verification of custom AI accelerator hardware, including matrix and vector engines. Develop SystemVerilog and UVM environments, reference models, assertions, checkers, coverage, and regressions. Verify numerical precision, quantization, data flow, performance, latency, and power efficiency across RTL blocks and complete systems. Collaborate with architecture, software, compiler, runtime, and systems teams through design bring-up, emulation, and pre-silicon validation.

Leads the architecture and development of customer-facing SDKs, APIs, CLI tools, examples, documentation, developer workflows, and portal integrations for an AI SoC platform. Partners across AI, multimedia, robotics, tools, release, and SQA teams to deliver reliable installation, deployment, compatibility, and release experiences. The role also hires, mentors, and leads engineers focused on developer tools, documentation systems, APIs, and sample applications.

Develop system-level performance models and simulation frameworks for AI workloads, software runtimes, and hardware architectures. Model SoC components, memory systems, and interconnects; analyze bottlenecks and architectural tradeoffs; and evaluate latency, throughput, scalability, and energy efficiency. Collaborate with hardware architects, software engineers, and ML teams to support hardware-software co-design and guide next-generation AI compute platform decisions.

Define and build the software architecture underlying Velaura’s Physical AI platform, including SDKs, runtimes, drivers, firmware, compilers, developer tools, and workload orchestration. Lead hardware/software co-design, performance optimization, profiling, and integration with ROS2, DDS, robotics, drones, and autonomous systems. Establish engineering processes and help develop a high-performing platform software team enabling efficient AI deployment across heterogeneous computing systems.