Artificial Intelligence Jobs in San Jose, CA

Create developer-focused technical content, demos, reference examples, deployment guides, and docs-as-code workflows to accelerate adoption of NVIDIA enterprise AI software. Work across engineering, product, field, and partners to design developer journeys, build sample applications and notebooks, enable sales and partners, capture feedback, and engage the open source and cloud-native communities.

Serve as the embedded systems expert for NVIDIA Physical AI partners, integrating sensors, deploying and optimizing robotics foundation models on Jetson-class edge hardware, guiding customers from prototype to production, and delivering reference implementations and workshops.

As a Principal Software Engineer, you will lead AI networking system transformations, manage complex customer engagements, and influence NVIDIA's networking technologies.

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.

Lead workload-driven AI/ML architecture across hardware and software: characterize workloads, perform system-level performance and memory analysis, define measurement and pre-silicon estimation methods, leverage MLIR/IREE toolchains, identify bottlenecks, recommend SoC/CPU/memory optimizations, and present findings to engineering leadership while mentoring junior engineers.

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

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.

Responsible for defining and validating SoC IP blocks, collaborating with teams to ensure quality silicon for AI applications, and mentoring junior engineers.

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.

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.

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.

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.

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.