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.

As a Backend Engineer at Brain Co., you will design, build, and operate backend services and data pipelines for AI products, focusing on robustness and scalability. You'll collaborate across teams and own the full service lifecycle while ensuring high availability and performance. You should have 3+ years of experience in production backend systems and a strong foundation in APIs and distributed systems.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

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 developing and enhancing a revenue cycle management platform, ensuring efficient data access for healthcare providers, and collaborating with clients to address their needs.

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

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

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

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.

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

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 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.

Design, build, and operate scalable, secure AI platform services that power Copilot (model serving, orchestration, retrieval, APIs). Collaborate with researchers and engineers, ship well-tested production code, run services in production (including on-call), and iterate quickly to deliver consumer AI features.

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.