Artificial Intelligence Jobs in Santa Clara, CA

The AI Robotics Research Intern will focus on dexterous manipulation, utilizing large-scale data to improve robotic behaviors through human interaction insights. Responsibilities include developing scalable frameworks, processing data, and optimizing robotic policies for physical deployment.

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 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.

Develop logic design and RTL coding for AI SoC products, integrate IP blocks, perform quality checks, and mentor junior engineers.

The Director of Product will lead AI-powered seller tools, driving strategy, delivering features, and managing a high-performing team to enhance seller experiences.

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.

Build and productionize AI applications integrating LLMs and vision models into an edge-to-cloud robotics platform. Fine-tune and quantize models, deploy to NVIDIA Orin/Jetson and cloud GPUs, integrate Vector DBs/RAG/agent frameworks, and create data pipelines to collect and version sensor data for retraining.

Lead product definition and roadmap for AI factory resilience platform features (telemetry, health-checks, observability APIs, deployment experience). Drive developer experience, open-source community collaboration, cross-functional alignment, and partner with engineering on design, prioritization, and delivery.

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

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 AI Business Analyst will partner with HR teams to identify AI use cases, support the AiMEE chatbot, collaborate on HR analytics, and enhance employee experience through technology solutions.

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

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.

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.

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.

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 enterprise-scale data and AI transformation by translating business needs into AI-ready data foundations, automating data flows, defining governance and metadata strategies, shaping data products, partnering with engineering to build scalable pipelines, and driving adoption and measurable business outcomes.

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.

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.

The Principal AI Architect will design intelligent agent systems, establish technical standards, and guide development for various industry verticals within Zoom's AI framework.

Lead full-cycle enterprise sales to exceed a $3-5M annual quota by acquiring new clients and expanding existing accounts. Position end-to-end AI and transformation solutions, convert engagements into managed services, build CxO relationships, drive strategic account plans, and collaborate cross-functionally to maintain pipeline integrity and deliver measurable business outcomes.

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.

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.