Artificial Intelligence Jobs in Oakland, CA

The Director of Digital Assurance & Transparency will lead AI solution teams, manage full-stack AI solutions, ensure quality, and foster innovation while driving business growth.

Lead the design and implementation of enterprise AI/ML solutions, oversee project performance, mentor leaders, and ensure compliance with Responsible AI.

The role involves designing and implementing AI/ML solutions, leading teams, mentoring staff, and managing project delivery while ensuring client satisfaction.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

Design, build, and deploy production-ready AI solutions across enterprise platforms. Implement LLM workflows (prompt engineering, embeddings, RAG), integrate with enterprise data and SaaS systems, ensure operational readiness (monitoring, logging, performance), and partner with Security, Privacy, IT, and business teams to meet governance and compliance requirements.

Lead enterprise change management for AI adoption and workforce transformation. Develop and execute change strategies, communications, adoption plans, enablement materials, coach change managers, enable champion networks, support AI governance and metrics, and drive cross-functional engagement for sustained adoption.

You will build and maintain AI-native application features, working with a team of AI and backend engineers to ensure quality and innovation.

Lead cross-functional teams to design and deliver commercial analytics projects for life sciences clients. Provide strategic guidance, analyze data, develop actionable insights, manage client communications and timelines, build/optimize data processes (including coding and visualization), support business development, and mentor junior staff.

Own and scale the Nx-based monorepo, build system, and CI/CD to make builds, tests, and developer workflows fast, reliable, and observable. Build developer tooling, local dev stacks, and production-grade developer infra with SLOs, on-call, and measurable DX improvements.

Lead end-to-end delivery of AI deployments for financial services clients: define features, translate business needs into technical specs, run agile sprints, drive adoption and change management, ensure measurable program outcomes, and expand commercial relationships while collaborating with engineers, product, design, and investment teams.

The Managing Partner will lead enterprise sales, managing multi-million dollar pipelines and fostering strategic partnerships with AI labs and Fortune 500 clients.

As a Full-Stack Engineer at Labelbox, you will develop and maintain AI tools, focusing on data training systems, intermediate user interfaces, scalable architectures, and work in a collaborative, fast-paced environment.

As a Staff AI Software Engineer, you will design and build autonomous AI systems for security testing, focusing on reliability, scaling, and integration with enterprise infrastructure, all while collaborating with top security experts.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

The Automation Specialist will design and implement automation solutions using AWS and AI-driven platforms like Amazon Bedrock to improve efficiency across enterprise workflows.

Manage and improve accounts payable by configuring, testing, and troubleshooting AI agents (e.g., Coupa Navi) to automate invoice processing, payment runs, and reconciliations. Perform day-to-day accounting: prepare journal entries, reconciliations, bank and investment records, vendor maintenance, and support month-end close, audits, and 1099 filings. Partner cross-functionally to expand P2P automation and provide internal/external accounting support.

Lead go-to-market strategy for Tanium Atlas and AI-powered automation and governance within the Autonomous IT platform. Define positioning and messaging, engage customers and market to inform product direction, create high-impact content and sales enablement, drive launches and competitive intelligence, and collaborate cross-functionally to increase awareness and adoption.

Lead AI technical strategy, architect AI systems, set safety standards, mentor engineers, and evaluate AI models at Midi Health, a healthcare-focused company.

You will design and build LLM-powered features, integrate AI capabilities into workflows, and contribute to internal practices at a healthcare-focused company.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

Develop and prototype AI-driven applications, validate ideas with real data, and transition projects into production while collaborating with teams and customers.

Design, develop, and maintain generative AI and cloud-based AI solutions using Microsoft Azure and Power Platform tools. Collaborate with cross-functional teams to gather requirements, build scalable AI applications, deploy and manage models with Azure Machine Learning, test/debug, document designs, and provide training and user support.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead end-to-end development and scaling of Jerry.ai's AI platform, designing prompt strategies, evaluation frameworks, and guardrails; translate technical tradeoffs for stakeholders; run experiments to improve automation, accuracy, and customer satisfaction; partner with OpenAI to deploy new models and voice/workflow automation.

Lead end-to-end product development for an AI agent and platform that automates customer interactions. Define roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI to deploy models and voice/workflow automation, drive experiments to improve automation rates, quality, and customer satisfaction, and establish AI best practices.