Artificial Intelligence Jobs in Santa Clara, CA

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.

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, 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.

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.

Lead the development of AI/ML deployment infrastructure, collaborate with engineers, develop pipelines, manage teams, and enhance data applications.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

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.

As an AI Platform Backend Engineer, you'll design and deploy backend systems utilizing AI for real-world applications, optimize performance, and mentor junior engineers while working directly with clients to address complex challenges.

As an AI Platform Engineer, you will design and develop AI-driven backend solutions, optimize performance, and collaborate with customers and teams.

The Senior Technical Program Manager is responsible for site and data center operations, leading cross-functional programs, ensuring operational readiness, and managing metrics and KPIs for AI infrastructure.

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 and implement AI-native frameworks, optimize performance for deep learning models, and ensure high-quality software development using modern C++ and AI tools.

Lead developer marketing programs to acquire, activate, and grow developers building AI applications on PC using NVIDIA SDKs and RTX. Create technical content, run community and partner activations, educate developers on platform capabilities, drive engagement and evangelism across channels, and use analytics to improve program performance.

This role involves designing and exporting AI frameworks, enhancing product capabilities, collaborating with teams, and directly engaging with customers.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

Seeking a Distinguished Technologist to lead AI architecture and design for HPE's Private Cloud AI offerings, focusing on applied AI, agentic AI, and generative AI. Responsibilities include technical leadership, implementation, and mentoring engineers while optimizing AI systems for cloud-based solutions.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

Lead strategy, roadmap, and delivery for HPE's enterprise AI software platform across deployment models. Drive modular architecture (inference, agent deployment, GenAI workflows, edge AI), define GTM and monetization, align cross-functional teams, and deliver executive-ready materials to bring AI products from early stages to revenue.

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.

Design, develop, and maintain engineering solutions to validate, monitor, and operate GPU clusters for ML researchers. Research and apply AIOps/Agentic AI to reduce operational toil, enable self-service reliability and performance improvements, and participate in on-call support for platform systems.

Build and optimize local LLM inference and agent runtimes for Windows and GeForce RTX GPUs. Profile and reduce latency/memory, integrate CUDA/TensorRT pipelines, implement sandboxed security and privacy routers, and harden multi-agent orchestration for constrained consumer hardware while collaborating across research, driver, and open-source teams.

Lead generative-data strategy and roadmaps for LLMs and visual models. Build end-to-end data pipelines (synthetic generation, HITL, RL loops), define data quality and evaluation frameworks, partner with researchers, engineers, customers, and legal/ethics to enable safe, scalable model training, fine-tuning, and deployment, and establish external partnerships for dataset access.