Artificial Intelligence Jobs in Visalia, CA

Lead strategy and execution for agentic AI product experiences across the DFIR portfolio. Define roadmaps, ship reliable and governed agentic features, drive adoption and growth, instrument metrics and experiments, run continuous discovery, and collaborate cross-functionally to ensure trustworthy, scalable AI-native products.

Design and implement an MLIR-based AI compiler for GPU inference: develop graph representations and dialects, optimize performance and memory, define APIs, generate kernels, and collaborate with framework and hardware teams to enable new model patterns and GPU features.

Design and deploy production ML/AI architectures on the Databricks platform, lead Generative AI (LLM, RAG, agent) implementations, optimize and scale MLOps pipelines, provide technical pre-sales support, and translate customer feedback into product improvements while mentoring peers and meeting delivery milestones.

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Lead engineering team to develop AIPerf, an open-source benchmarking tool for LLM inference performance, while overseeing technical direction and mentoring engineers.

Partner with sales and engineering teams to design and implement large-scale data and AI architectures on the Databricks platform. Deliver 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.

Lead design, build, and deployment of production-grade generative AI products (LLM apps, agents, copilots, RAG/search). Rapidly prototype features, build APIs, services, and retrieval pipelines, optimize performance and reliability, ensure responsible AI, translate research into product, and mentor team members.

As a senior software engineer at NVIDIA, you will design and build tools for performance analysis of AI workloads, lead automation efforts, and enhance workflows for engineers.

Lead and deliver large, high-risk HPC and AI deployments across the Americas. Manage customer relationships, project financials and P&L, cross-functional teams, schedules, legal/commercial issues, and on-site acceptance. Mentor team members and provide performance input while coordinating internal and external resources.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Lead 0-to-1 exploratory bioinformatics projects that combine genomic data, literature retrieval, deterministic methods, and AI to prototype new product capabilities, define evaluation and stop criteria, diagnose failures, and hand off validated evidence packages to Product, Engineering, and Bioinformatics for independent evaluation and potential productionization.

Own the member-facing mobile (and near-term signed-in web) product roadmap for AI-powered genomic experiences. Drive activation, engagement, retention, and trust by translating intelligence-layer capabilities into polished consumer UX, rapid prototypes, experiments, and clear PRDs while partnering with AI platform, engineering, bioinformatics, design, and customer success.

Design and implement runtime protocols, formats, and systems to capture, serialize, stream, and replay interactive 3D content across engines. Own C++ runtime and C# APIs across editor, build, simulation, streaming, and playback. Optimize for low-latency, determinism, memory and bandwidth constraints, and build AI interfaces for inspecting and invoking engine state.

Architect and demonstrate simulation workflows using DSX Sim and Omniverse libraries to help partners and customers build, validate, optimize, and operationalize AI factory digital twins. Drive integration of Omniverse libraries, develop reference architectures, prototypes, benchmarks, and sample workflows, and coordinate with sales, product engineering, and partners to resolve blockers and advance adoption.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.

Provide strategic legal counsel for global AI products, ensuring compliance with evolving AI safety and privacy laws, enabling sales and product teams, engaging regulators and industry groups, driving AI trust initiatives, applying legal technology to streamline reviews, and mentoring junior lawyers to support product growth while mitigating risk.

Design and scale Kubernetes-native, GPU-accelerated cloud architectures for large-scale robotics simulation, synthetic data pipelines, training, and inference. Advise partners, optimize scheduling, storage, networking, and GPU utilization, and drive adoption of NVIDIA Physical AI frameworks while collaborating with product and engineering teams.

Develop and maintain AI networking communication frameworks, production benchmarking tools, and automation for large-scale AI training/inference. Enable models in benchmarking infrastructure, analyze end-to-end performance, perform large-scale parameter searches, and collaborate with networking and hardware teams to co-design features and optimize system performance.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

The role involves deploying AI networking solutions in data centers, driving architectural decisions, troubleshooting network issues, and collaborating on customer projects for NVIDIA's technology solutions.

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.