Artificial Intelligence Jobs in Sunnyvale, CA

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.

Evaluate and optimize NVIDIA AI infrastructure by running multi-node training/inference on large GPU clusters, designing benchmarks, profiling deep-learning workloads, identifying bottlenecks, producing tutorials and whitepapers, analyzing competitors, and presenting demos at major conferences.

Lead development and scaling of a customer-facing AI agents platform using LLMs and internal APIs. Define roadmap, prompt strategies, evaluation frameworks, guardrails, and experiments to improve automation, accuracy, latency, and customer satisfaction. Partner with engineering and OpenAI to deploy voice and workflow automation capabilities.

Lead development and scaling of a customer-facing Agentic AI platform using LLMs and agents. Define roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI, run experiments to improve accuracy, latency, cost, and automation rates, and translate technical work across the organization.

Lead and own the AI platform strategy: consolidate prompts across systems, prioritize chatbot work, drive architectural decisions, manage OpenAI/ChatGPT integrations, and translate technical tradeoffs into execution while learning prompt workflows and coordinating engineering, data, and operations teams.

Lead development and scaling of an AI agent platform to automate customer interactions. Define roadmap, partner with engineering on prompt strategies, evaluation frameworks, and guardrails. Translate technical design across the org, run experiments to improve quality and automation, and work with OpenAI on voice and workflow model integrations.

Lead consolidation and strategy for a fragmented AI prompt platform, prioritize chatbot and agent work, own OpenAI partnership and ChatGPT presence, and drive platform architecture, migration, and cross-functional execution to scale AI-powered customer experiences.

Lead development and scaling of Jerry.ai’s agentic AI platform and customer experiences. Define roadmap, design prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and external partners (e.g., OpenAI) to deploy voice and workflow automation models, drive experimentation to improve quality and automation, and establish AI best practices and platform standards.

Lead the customer-facing Agentic AI product and unify fragmented LLM-driven systems. Own roadmap, partner with engineering on prompt strategies, evaluation frameworks, and guardrails. Translate technical design across the organization, run experiments to improve quality and automation rates, and collaborate with OpenAI to deploy next-generation voice and workflow models.

Lead end-to-end development and scaling of a customer-facing agentic AI platform using LLMs, agents, and internal APIs. Define prompt strategies, evaluation frameworks, guardrails, and experiments to improve automation, accuracy, and customer satisfaction. Serve as technical translator between engineering and product, work with partners like OpenAI, and drive production-grade workflows and integrations.

Lead end-to-end development and scaling of an agentic AI platform that automates customer interactions. Define roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI to deploy voice and workflow automation, improve answer quality and automation rates, and establish AI platform standards across the organization.

Lead the end-to-end development and scaling of a customer-facing agentic AI platform using LLMs and agents. Define prompt strategies, guardrails, evaluation frameworks, and API designs. Translate technical work across product and engineering, run experiments to improve accuracy and automation rates, and partner with OpenAI on voice and workflow model integrations.

Lead end-to-end development of Jerry.ai's agentic AI platform and customer experiences. Define roadmap, prompt strategies, evaluation frameworks and guardrails; partner with engineers and OpenAI; drive experimentation to improve automation, accuracy, and customer satisfaction.

Lead consolidation and strategy for a fragmented AI prompt/platform stack, own technical prioritization for chatbot/agent features, manage vendor relationships (e.g., OpenAI/ChatGPT), drive architectural decisions, coordinate engineering/data/ops to deliver production-grade LLM-powered customer experiences, and maintain day-to-day platform reliability.

Lead development and scaling of Jerry.ai's AI agent platform and customer experiences. Define roadmap, design prompt strategies, evaluation frameworks, and guardrails. Act as technical translator between engineering and the business, run experiments to improve automation, quality, and satisfaction, and partner with OpenAI on voice and workflow model integrations.

Lead development and scaling of a customer-facing agentic AI platform. Define roadmap, design prompt strategies, evaluation frameworks and guardrails, collaborate with engineers, run experiments to improve automation and satisfaction, and partner with OpenAI on voice and workflow model integrations.

Lead end-to-end development and scaling of a customer-facing Agentic AI platform using LLMs and agents. Define prompt strategies, evaluation frameworks, guardrails, and experimentation to improve automation, answer quality, and customer satisfaction. Partner with engineers and OpenAI to deploy voice and workflow models and serve as technical translator across the organization.

Lead consolidation and platform strategy for Jerry.ai's AI systems: unify prompt sources, prioritize chatbot technical work, drive architecture decisions, manage partnerships (e.g., OpenAI/ChatGPT), and coordinate cross-functional teams to deliver scalable, production-grade agentic AI products.

Lead and grow a hands-on AI Products engineering team building LLM and agent capabilities. Own technical vision, evaluation practices, and production delivery. Split time between management and coding, drive 0-to-1 product work, and ensure model quality, reliability, and upgrade pipelines.

Design and build full-stack AI prototypes; decompose products into model, retrieval, prompting, agents, and orchestration components; integrate APIs, databases, and cloud; define evaluation loops; perform error analysis; collaborate cross-functionally; and present results to founders and partners.

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 RTL design and microarchitecture for SoC IP blocks, implement and integrate Verilog/SystemVerilog RTL, collaborate with verification and physical design teams for timing/power closure, drive silicon bring-up and post-silicon validation, mentor junior engineers, and ensure secure development and quality assurance for SoC/ASIC projects.

Develop RTL (Verilog/SystemVerilog) for AI SoC designs, integrate IP blocks, optimize logic for PPA and timing, collaborate with verification and physical design teams, support silicon bring-up and post-silicon validation, and follow secure development practices.

Lead vision and strategic architecture for enterprise AI and analytics on Everpure's all-flash platform. Define data/metadata orchestration, build ecosystem partnerships, drive technical competitive strategy, and act as spokesperson to executive leadership and customers to enable AI-ready, governed data pipelines across edge, core, and cloud.

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.