Artificial Intelligence Jobs in Visalia, CA

Founding technical role to design, build, and deploy internal AI tools and agents using LLMs and retrieval systems. Partner with investors and the data team to accelerate sourcing, diligence, portfolio insights, and define CRV's AI strategy through rapid prototyping and production-grade systems.

Design and build AI-driven interactive prototypes and production-ready flows using AI coding tools. Own prompt-to-pipeline solutions, validate via experiments and user research, produce high-fidelity UX in Figma, and collaborate with engineering to move prototypes to production while aligning business, design, and tech.

Own strategy and execution for a portfolio of AI-native and hyperscale customers. Serve as primary relationship owner, drive revenue, market share and product adoption, coordinate with solution architects, product marketing, OEMs and partners, forecast and train customers, and evangelize NVIDIA platforms to accelerate enterprise growth.

Lead CRG-wide AI risk, governance, and compliance as an independent control function. Define risk-tiering, embed governance-by-design into model lifecycle tooling, partner with Legal/Privacy/InfoSec/Quality, and operationalize scalable controls, templates, and automated monitoring to ensure regulatory and enterprise standards for AI-enabled clinical research solutions.

Research and develop GPU-accelerated techniques for AI and financial workloads. Analyze and optimize performance on CPU/GPU architectures, collaborate with industry and academia, publish findings, and influence hardware and software design while supporting developer adoption.

As a Senior Software Architect for AI Networking, you'll design and implement AI networking technologies, optimize data processing in AI systems, and collaborate on cutting-edge solutions.

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.

The Technical Account Manager will lead customer technical projects, provide architecture guidance, ensure product adoption, and serve as a trusted advisor to enhance customer success and satisfaction.

As an AI Solution Engineer, you will architect and demonstrate AI solutions, lead technical discussions, and assist customers with AI outcomes in pre-sales engagements.

Partner with sales to design and implement big data and ML architectures on Databricks. Build proofs-of-concept, validate cloud and third-party integrations, advise customers on best practices, and promote open-source projects like Spark, Delta Lake, and MLflow.

Partner with the CEO/COO to translate strategy into execution, build AI-enabled operating systems, drive cross-functional programs, run OKRs and planning cadences, scale vendor/procurement, and lead high-priority projects end-to-end.

Own the long-term vision, roadmap, and execution of Sequencing's AI platform to turn genomic data into conversational, personalized health guidance. Lead AI-native product strategy, evaluate and productize emerging models and agents, partner with engineering and bioinformatics, run experiments and analytics, and optimize engagement, retention, and trust for mobile and web experiences.

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

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

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.