Artificial Intelligence Jobs in Sunnyvale, CA

Manage strategy creation, sales account management, and relationship building for Akamai. Drive revenue growth and improve customer relations in the telecommunications sector.

Build and scale core AI-native healthcare products across the stack, focusing on real-time audio processing, NLU/transcription, clinical documentation, EHR integrations, and distributed production systems. Drive architecture, ship features with high ownership, and operate in a fast-moving, regulated environment.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Serve as a technical advocate connecting Intel AI platforms with global developer communities through presentations, content creation, developer advocacy, ecosystem growth, and feedback to influence product roadmaps and improve developer experience.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

The AI Marketer/DevRel will enhance brand awareness through technical content, community engagement, and storytelling while fostering developer relations.

The internship involves multiscale modeling focusing on physics and AI modeling. Interns will run simulations and define workflows using Python and related libraries.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

The AI Artist will enhance AI-generated videos, develop scalable prompts, maintain high output quality, and explore emerging AI tools.

The Senior Software Engineer will build AI solutions for clinical workflows, collaborating with teams to enhance software architecture and mentor other engineers.

The internship involves researching emotional labor in difficult workplace conversations, evaluating AI platforms, and prototyping solutions within an interdisciplinary team focused on organizational behavior and wellbeing.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

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.

As an AI Success Manager, you'll lead customer deployments, execute success plans, and ensure business outcomes through AI technology. Responsibilities include technical guidance, project management, and cross-team collaboration to enhance customer satisfaction and adoption.

Own product lifecycle for Edge AI suites and tools: define value proposition and requirements, lead cross-functional teams, prioritize features, engage developers and partners, drive adoption, and support launches and marketing for open-source edge AI and robotics solutions.

Lead product strategy, roadmap, and go-to-market for NVIDIA enterprise game development solutions. Partner with engineering, ISVs, OEMs, and sales to integrate RTX, generative AI, and developer workflows into studio pipelines. Drive positioning, demos, trade-show strategy, sales enablement, and lighthouse case studies to grow adoption among game developers and publishers.

Lead and grow a team to design and deliver end-to-end AI hardware/software systems and AI GPU platforms. Define product strategy and multigeneration roadmaps, align cross-functional stakeholders, translate customer insights into requirements, ensure software enablement, and drive execution and innovation across silicon, systems, manufacturing, and go-to-market.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Perform Chinese annotation, labeling, and quality review of multimodal datasets (text, image, video, audio) for generative AI. Evaluate AI prompts/responses, conduct QA, identify edge cases, refine guidelines, and collaborate with teams to improve annotation workflows and tooling.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Lead and manage a team of Chinese (zh-CN) multimodal annotators and reviewers to meet throughput and quality targets. Own guidelines, calibration, audits, reporting, onboarding, and cross-stakeholder coordination for multimedia labeling workflows.

Evaluate AI outputs, perform audit-based reviews, identify patterns and failure modes, apply and improve annotation/evaluation guidelines, document findings, and communicate insights to cross-functional teams to improve model performance and evaluation frameworks.

Evaluate AI model outputs, perform audit-based reviews to identify patterns and failure modes, apply and refine guidelines, document findings, and collaborate with leads and cross-functional teams to improve model evaluation and data quality.