Artificial Intelligence Jobs in San Jose, CA

Leads business operations for Cisco’s Americas Hyperscale and AI Sales organization. Responsibilities include cross-functional planning, large-deal approval, go-to-market strategy, lifecycle optimization, market and portfolio planning, account penetration, change management, and technical program management. Partners with executive leadership, Finance, Legal, Supply Chain, Business Units, and strategic enterprise customers to accelerate deal execution and support long-term growth.

Design, build, and deploy LLM-powered validation pipelines and AI integrations for silicon design and post-silicon validation. Lead cross-team AI projects from prototype to production, evaluate emerging AI tech, and measure impact with data systems to improve workflows and scalability across chip development.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

This role focuses on driving adoption and alignment for ODM partners, supporting data center delivery, and incorporating partner feedback into product strategy.

Architect, build, and deploy LLM-powered AI systems and automation to accelerate post-silicon validation and toolchain workflows; integrate AI across engineering teams, evaluate emerging frameworks, measure impact, and operationalize solutions from prototype to production while supporting silicon bring-up and lab debug.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, AI agents, and orchestration frameworks. Build scalable AI services, models, APIs, and integrations with PLM, SAP, and other enterprise systems supporting engineering and manufacturing workflows. Collaborate with business and IT teams throughout the SDLC, improving solution quality through prompt engineering, evaluation, optimization, and production support.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Lead design and implementation of Workday HCM and Compensation solutions. Collaborate with HR and IT stakeholders, provide technical leadership, optimize Workday architecture, troubleshoot complex issues, and drive continuous improvement and adoption of best practices.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Architect and scale high-cardinality observability and telemetry infrastructure for AI cloud platforms. Build Kubernetes exporters, eBPF diagnostics, hardware and network monitoring, dashboards, alerting, and GPU/network utilization billing pipelines. Establish telemetry standards for AI workloads, support proactive remediation, collaborate with GPU and scheduling teams, lead architecture reviews, and mentor engineers. The role requires expertise in Go, Kubernetes, Prometheus/OpenTelemetry, Linux performance tuning, eBPF, AI hardware metrics, and large-scale cloud or HPC telemetry systems.

Lead the architecture and implementation of AI workload scheduling and orchestration for a large-scale NeoCloud platform. Build batch and gang scheduling, admission control, queueing, topology-aware placement, GPU sharing, and multi-tenancy capabilities using Kubernetes technologies. Optimize NVLink and InfiniBand utilization, integrate scheduling with hardware and storage systems, improve reliability in HPC environments, resolve contention and deadlocks, and mentor engineers while guiding architectural decisions.

Design, implement, and maintain CI/CD and MLOps pipelines, provision and scale cloud-native AI infrastructure (Kubernetes, GPU clusters), enforce IaC practices, implement observability and security frameworks, lead incident response and cross-functional platform engineering to ensure high availability and compliance.

Architects and operates high-performance storage infrastructure for AI training and inference. Responsibilities include developing Kubernetes CSI drivers, integrating GPUDirect Storage, optimizing NVMe caching and I/O performance, supporting RDMA and high-speed networking, monitoring storage systems, managing quotas and multi-tenancy, and mentoring engineers.

Manages software product and engineering activities from definition and planning through execution, production, maintenance, and end of life. Coordinates architects and cross-functional engineering teams, evaluates requirements, validates product definitions, resolves technical gaps, develops product strategies, identifies opportunities, and aligns milestones with business goals. Serves as a central liaison among development, testing, quality, sales, marketing, customers, and executives while overseeing large-scale technical programs.

Lead developer-focused performance optimization for RTX and DGX SoC platforms: collaborate with partners and internal teams to optimize CPU, AI, and 3D-graphics workloads, profile and debug CPU/GPU performance, influence next-gen SoC design, and mentor junior engineers. Occasional travel for conferences and partner site visits.

Drive revenue growth and market share for Renesas semiconductor solutions across AI, humanoid robotics, physical AI, edge computing, and IoT markets in the San Francisco Bay Area. Responsibilities include strategic account planning, customer and executive relationship management, cross-selling embedded, analog, power, and software solutions, developing design wins, managing forecasts and pipelines, negotiating agreements, and providing market intelligence to guide product and business strategy.

Build and ship production agentic systems for Roku’s streaming platform, including multi-step agents, internal tooling, real-time telemetry processing, distributed services, and hardware-in-the-loop workflows. Engineers own features across platform, application, UI, and cloud integration while working with AI tools on unfamiliar codebases. The role requires strong systems or infrastructure fundamentals and an existing shipped agentic project used by others.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Train and evaluate machine learning and deep learning models, diagnose training and generalization issues, conduct experiments, and develop reusable research tooling. The intern may work on LLMs, transformers, generative AI, fine-tuning, and evaluation workflows while rapidly prototyping with AI-assisted development tools. The role requires advanced quantitative coursework and graduate study in computer science, AI, machine learning, statistics, applied mathematics, data science, electrical engineering, or a related field.

Architects, builds, and operates enterprise AI platform services across model serving, inference optimization, evaluation, agentic infrastructure, APIs, and air-gapped deployments. Leads model development, fine-tuning, productionization, MLOps, security governance, and responsible AI practices. Establishes rigorous evaluation and release criteria, mentors senior engineers, drives technical roadmaps, and bridges AI research with reliable production engineering across cloud, on-premises, and resource-constrained environments.

Reviews and scores robotics data episodes using standardized quality rubrics. Identifies failures, unsafe behavior, edge cases, and recurring quality trends; documents findings and provides actionable feedback. Collaborates with stakeholders and teleoperators to improve review workflows, standards, training, and documentation while meeting throughput and accuracy targets.

The Senior Lead AI Engineer will develop and support AI software components, lead cross-functional teams, and optimize AI solutions for Capital One's banking systems.

Lead AI Engineer responsible for developing AI software components, partnering with cross-functional teams to deliver AI-driven products, and optimizing large-scale AI systems.

The Senior Lead AI Engineer will develop AI software components, partner with cross-functional teams, and contribute to AI system optimization. Key responsibilities include model training, deployment, and leveraging AI technologies to enhance banking services.

The Lead AI Engineer will design, develop, and support AI software components, optimizing AI systems for performance and scalability while collaborating with cross-functional teams.