Artificial Intelligence Jobs in Sunnyvale, CA

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.

Embed with customers to learn their workflows, redesign and build production GenAI/LLM systems on AWS, ship and operate code, evaluate non-deterministic models, and own business outcomes.

The role involves synthesizing, placing, and routing high-speed designs, collaborating with RTL teams, and optimizing designs for power/performance area.

As an AI Engineer, you will develop new AI products, iterate with a small team, and apply various GenAI techniques to enhance educational experiences.

Manage AI compute clusters, monitor systems health, optimize resources, and troubleshoot technical issues, ensuring high performance for ML applications.

Design, build, and deploy LLM-powered tools, RAG-based knowledge systems, and agentic workflows to automate SOC integration tasks. Own reliability, evaluation, and observability for AI systems and collaborate with SOCD teams to scope, validate, and measure productivity gains from deployed automation.

Lead end-to-end technical programs to integrate AI (LLMs, agents, chatbots) into chip design workflows. Plan, coordinate, and drive cross-functional execution across ASIC, platform, and third-party partners, measure program metrics, manage risks, and translate user requirements into scalable production-ready AI capabilities for silicon development.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

The Investor role at Samsung Next focuses on sourcing and evaluating early-stage investments in AI and robotics. Candidates will develop investment skills under experienced guidance, participate in deal discussions, and support startup founders. A technical background in related fields is essential, along with strong analytical and communication skills.

The role involves driving the architecture and evolution of the Search product at Databricks, collaborating across teams, mentoring engineers, and ensuring high-performance systems for AI applications.

As a Senior Staff Applied AI Engineer at Databricks, you will design and build retrieval systems, optimize search for both LLMs and humans, and lead technical direction and team growth.

Intern will design and optimize software to deploy large-scale AI models on production vehicles, profile and improve inference across compute, memory, and I/O, reduce latency and power use on embedded automotive platforms, and deliver production-ready, fleet-scalable performance optimizations.

The Risc-V Architect will analyze AI workloads, design custom RISC-V cores, and lead the architecture efforts at Tenstorrent, improving CPU performance.

Design and implement high-performance Vision AI pipelines and large-scale distributed services for processing video, image, and 3D data across edge and cloud. Build multi-modal perception, use simulation/synthetic data for testing, profile and optimize GPU-accelerated inference, collaborate with research and product teams, drive code quality and reviews, and mentor engineers to productionize vision models at scale.

Build and operate agentic AI tutoring systems, including multi-step tutoring loops, tool use, learner memory, planning, verification, guardrails, and evaluation harnesses. Develop evidence-backed learner models and measure conversational and teaching quality using real learner data. Partner with the founding team to define product behavior, translate ambiguous questions into measurable systems, and deliver end-to-end improvements. The role requires production LLM experience, strong Python or TypeScript/Node.js skills, and independent software engineering ownership.

Design and oversee datacenter-scale AI infrastructure architectures across networking, compute, storage, and Kubernetes ecosystems. Lead prototyping, testing, performance optimization, and lifecycle refinement for scalable AI products. Collaborate with hardware, software, security teams, customers, and partners to develop proofs of concept and MVPs. Produce technical documentation, user guides, whitepapers, blogs, and curricula while translating complex systems concepts for diverse audiences.

Designs, deploys, maintains, and tunes large-scale HPC and AI clusters. Responsibilities include workload scheduling, infrastructure automation, CI/CD pipelines, monitoring, logging, alerting, storage and networking deployment, bottom-up troubleshooting, methodology documentation, and R&D support. The engineer works across hardware, operating systems, GPU computing, virtualization, cloud platforms, and software stacks to deliver performant, reliable self-service infrastructure.

Lead product strategy and multi-quarter roadmaps for NVIDIA’s AI Factory software-defined networking stack. Guide upstream development across OVN, OVS, and OVN-Kubernetes; define requirements, priorities, and architecture trade-offs; coordinate dependencies across DPU, fabric, virtualization, and engineering teams; and translate partner and operator needs into product releases, reference deployments, and community strategy.

Lead global co-marketing strategies and campaigns with AI ecosystem, Neocloud, and AI ISV partners. Develop joint value propositions, manage partner relationships, coordinate cross-functional teams, oversee budgets and vendors, measure campaign ROI, maintain program governance, enable partners, protect brand integrity, and present results to executives. The role focuses on translating Intel hardware and software capabilities into customer-centric AI and cloud solutions.

Build and own an agentic creative pipeline that generates, vets, and optimizes thousands of image and video ad variants daily: extend LLM critic feedback to multimodal assets, build fast cost-efficient video pipelines, route generative models by cost/quality, train and deploy brand-style checkpoints, implement policy pre-flight checks, and ship event-triggered ad pipelines with A/B validation in production.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Develop and test resilient, scalable multi-agent AI systems and RAG pipelines. Integrate agents with external tools and APIs, run LLM/RAG evaluations, write prompts, and support light model fine-tuning to improve network security product development efficiency.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.