Artificial Intelligence Jobs in Sunnyvale, CA

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.

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.

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.

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.

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.

The AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

As an AI Research Engineer, you will work on developing AI video ad models, collaborating with teams, and contributing to research for publications.

The role involves developing AI systems software, implementing and optimizing kernels for AI workloads, and collaborating on deep learning frameworks and libraries.

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 a customer-focused engineering team in AI Networking, enhancing NVIDIA's networking capabilities and supporting customer initiatives in large-scale AI infrastructure.

The Solutions Architect will design GPU-accelerated data pipelines, develop AI workflows, and serve as a liaison between NVIDIA and OEM partners while addressing high-performance computing needs.

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

Responsible for productizing software stack for AI compute engine; focusing on development, optimization, and scaling of AI deployment software, collaborating with hardware and software teams.

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.

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

As an RTL Design Engineer, you'll develop RTL coding and simulation for SoC designs, optimizing logic for performance and power. You will collaborate with verification teams, implement microarchitecture features, and ensure design integrity in a fast-paced environment.

Develop and maintain automated regression test suites (Python, JavaScript), integrate tests into CI/CD, optimize test cycles for firmware and receiver interaction, collaborate cross-functionally, and present frontend test results to stakeholders to ensure high-precision GNSS positioning reliability.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Lead RTL development and microarchitecture of an AI-enabled RISC-V CPU core, including vector and matrix engines. Drive design for performance, power, area, verification support, and SoC integration while collaborating with cross-functional teams to meet timing, reliability, testability, and power goals.

Lead cross-functional initiatives to increase engineering productivity via AI-assisted development tools, workflows, and dashboards. Drive adoption, define productivity metrics, enforce governance and cost controls, coordinate automation (test generation, incident workflows), run enablement programs, and report executive-level impact and roadmap progress.

The Solutions Architect will develop AI applications at scale, focusing on machine learning, deep learning, and generative AI. Responsibilities include integrating enterprise data, providing feedback for software improvements, and collaborating with engineering teams.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.