Artificial Intelligence Jobs in Santa Clara, CA

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.

Lead and conduct original research in embodied AI for autonomous systems, developing and evaluating large-scale perception, decision-making, and control models. Translate research into deployed prototypes, collaborate with engineering and field teams, support field tests and data collection, and define research strategy across reinforcement learning, vision-language-action, sim-to-real, memory, and multi-agent coordination.

Develop and deploy multimodal foundation models and agentic AI for multi-robot coordination. Design training recipes, curate datasets, build simulation and RL environments, evaluate performance, and integrate models into real-time robotic systems with field trials.

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.

The Technical Solutions Engineer resolves customer issues, troubleshoots complex network environments, and collaborates with engineering teams to ensure customer satisfaction.

The Solutions Architect will implement ERP projects focusing on NetSuite integration, optimize functionalities, and ensure system reliability while collaborating with stakeholders.

Research, design, and train foundation models for robotics; align text-based reasoning with low-level control policies; evaluate zero-shot generalization; run experiments; integrate models into robotic systems and present findings externally.

Design and architect multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows and autonomous operations.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tool use, and support RLHF alignment and constitutional AI development to ensure safe AI deployment.

Lead product strategy and roadmap for AI and robotics products, translating VLA and agentic RAG breakthroughs into customer value, defining safety/usability/performance metrics, working with enterprise adopters, and driving cross-functional delivery.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Optimize, compress, and distill large language and vision models for on-device inference. Build pipelines for distillation and hardware-specific compilation, and benchmark performance across NPU/GPU architectures.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

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.

Develop and deploy end-to-end AI/ML solutions focused on GenAI and LLMs. Build and fine-tune models, create embedding search and agentic systems, perform forecasting and data mining, collaborate with stakeholders, and drive improvements in supply chain and material demand forecasting.

Lead enterprise AI, GenAI, agentic automation, MLOps, and RPA strategy and delivery. Build multi-cloud LLM platforms, enable model lifecycle management, deploy multi-agent systems, run an RPA COE, partner with security and legal, and drive production-scale AI programs with measurable business impact.

Design, deploy, and maintain large-scale AI training and inference infrastructure. Scale distributed training, optimize GPU/hardware utilization, manage multi-cloud compute and orchestration, maintain MLOps tooling (MLflow), build high-throughput data pipelines, and partner with researchers and software engineers to productionize and monitor LLMs and other AI models.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Build production-grade AI agent systems, automations, and workflows across recruiting, security/compliance, finance, onboarding, CRM integrations, and growth. Implement LLM-powered solutions, API integrations, and operational tooling to improve hiring, sales, and enterprise readiness.

The role involves applying diffusion techniques and developing AI models to enhance 3D reconstruction pipelines for various industries.

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