Artificial Intelligence Jobs in San Jose, CA

Lead the architecture and deployment of LLM-powered conversational chatbot systems, optimizing performance and ensuring scalability in a crypto environment.

Lead the product management for HPE's GreenLake Cloud Platform, overseeing strategy in AI-driven innovations and user experiences while managing a team and collaborating with cross-functional stakeholders.

The Director of Business Development will drive growth in the Physical AI sector by identifying market opportunities, managing strategic partnerships, and achieving revenue targets through effective sales strategies.

Lead the development and integration of advanced AI-driven software systems for next-generation electric vertical takeoff and landing aircraft, ensuring reliability and safety in collaboration with cross-functional teams.

The Senior Software Engineer will optimize AI workloads using machine learning techniques for large scale GPU and CPU clusters, focusing on resource allocation, performance analysis, and collaboration on hardware/software teams.

The role involves defining product vision and strategy for in-vehicle AI experiences, leading product execution, integrating AI, and partnering with engineering and automotive customers for successful deployment.

The Senior Staff Physical AI Data Algorithm Engineer is responsible for designing and optimizing a vehicle-cloud data closed-loop architecture, managing data toolchains, and ensuring high-quality data standards throughout the model development cycle.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

Lead end-to-end evaluation and benchmarking of NVIDIA's Generative AI (Nemotron) models for strategic ISV partners. Design evaluation frameworks, experiments, and metrics; translate results into go/no-go recommendations; deliver dashboards and executive readouts; partner with research, product, and engineering to set release criteria and drive model-improvement signals and roadmap priorities.

Partner with HR COEs to identify and prioritize AI use cases, evaluate and optimize Workday and AiMEE capabilities, lead pilot deployments, collaborate with data science and GIS teams to build HR analytics and AI solutions, and support planning, testing, and deployment to improve employee experience and process automation.

Lead and transform the US pitch and pursuit process by embedding AI enablement across stages. Manage a searchable global pitch content library, maintain structured experience and CRM data, create reusable pursuit assets and prompt frameworks, and develop training and adoption materials. Partner with BD, lawyers, and global teams to improve workflow, support high-priority pitches, and oversee directory and awards submissions to increase speed, quality, and consistency of wins.

Lead and grow a team of software product managers for HPC/AI solution software. Own people leadership, drive consistent product practices, align roadmaps with architects and engineering, ensure delivery discipline, mentor talent, and shape portfolio-level strategy and execution.

Design and build scalable backend services and APIs for HPE EdgeConnect Orchestrator, integrate AI/LLM-driven workflows and Copilot capabilities, work with analytics and telemetry for real-time insights, and ensure quality through testing, debugging, and performance optimization.

The Helix AI Engineer will design and develop multimodal autonomous agents, focusing on reasoning and reliability for humanoid robots using machine learning techniques and frameworks.

Design and build AI orchestration architecture using Claude Code; create reusable orchestration patterns (skills, subagents, tools, hooks); optimize for latency, error handling, observability, and testing; run experiments, document learnings, and deliver production orchestrators that accelerate platform development.

Define sensor-to-actuator Physical AI reference architectures; analyze and partition AI, vision, perception, planning, and control workloads across CPUs, DSPs, FPGA fabric, GPUs, and AI accelerators; develop workload models and benchmarks; drive architecture tradeoffs and influence next-generation SoC, FPGA, memory, interconnect, and accelerator roadmaps.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Profile, benchmark, and optimize large-scale LLM training and inference across GPUs, CPUs, HCAs, and switches. Focus on collective communication and high-performance networking, build PyTorch trace-based profiling and replay tools, define performance test plans, and collaborate across hardware and software teams to identify bottlenecks and achieve performance targets.

Design and build AI-native, high-performance CI/build/test/validation infrastructure for large Tegra C/C++ codebases. Optimize build graphs, caching, incremental builds, remote execution, device-lab testing, and integrate reasoning agents for autonomous triage, remediation, and self-healing workflows. Package reusable platform capabilities, drive adoption, and measure developer feedback loop improvements.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core, including vector and matrix engines. Define features, optimize performance/power/area, support verification and simulation, collaborate with CPU modeling and physical design teams, and ensure design meets timing, reliability, testability, and power goals.

Operate, automate, and ensure reliability of colocation, on-prem GPU clusters, and cloud environments. Build IaC (Terraform/Ansible), automation, monitoring (Prometheus/Grafana, DataDog), run on-call incident response, produce RCAs, and support customer-facing platform services and documentation.

Lead and build an SRE function for AI accelerator infrastructure across colocation, on-prem lab clusters, and multi-cloud. Hire and manage a small SRE team, define SLOs/SLIs, own observability and incident management, drive IaC and automation (Terraform/Ansible), manage storage and HPC integrations, and align reliability with DevOps and executive stakeholders.

Design, build, and tune hybrid retrieval and RAG systems combining Elasticsearch, vector/embedding search, and re-ranking models. Improve relevance using query analytics, behavioral signals, experimentation, and evaluation metrics to enhance search quality and Generative AI responses. Collaborate with AI engineers, platform teams, and product stakeholders.

As a Full-Stack Engineer at Labelbox, you will develop and maintain AI tools, focusing on data training systems, intermediate user interfaces, scalable architectures, and work in a collaborative, fast-paced environment.

Lead product strategy and execution for AI-driven Copilot, agentic AI, and workflow orchestration on the Pure1 platform. Define roadmap, drive integrations, expand SaaS subscriptions, and partner with engineering, GTM, and customers to deliver automation and AI-powered infrastructure services.