Artificial Intelligence Jobs in Berkeley, CA

Lead large Microsoft Dynamics 365 (CRM/CE) implementations as a Senior Manager: engage senior clients, design functional solutions, oversee delivery lifecycle, coach teams, refine processes, and ensure operational excellence and adherence to firm standards.

Lead design and implementation of Microsoft Dynamics 365 ERP solutions, manage large-scale projects, engage senior clients, build and coach high-performing teams, resolve complex challenges, and drive operational excellence and strategic growth for the firm.

Lead design, build, and operation of scalable, secure AI-enabled platforms; mentor engineers; ensure reliability, security, and delivery of conversational AI and cloud-native solutions; collaborate cross-functionally to translate use cases into platform capabilities.

Lead design and delivery of conversational and agentic AI solutions for client contact centers. Manage teams, mentor junior staff, gather requirements, design UIs, run usability testing, and implement AI integrations to improve customer experience and product adoption.

Lead design and delivery of conversational and agentic AI solutions for client contact centers. Guide cross-functional teams to build NLP interfaces, prototype UIs, run usability testing, ensure data integrity and security, and advise clients strategically. Coach teams, manage large projects, and drive adoption of AI-powered contact center technologies to improve client experience and operational outcomes.

Lead Microsoft Dynamics 365 CRM implementations, architect solutions, manage project lifecycles and client accounts, mentor teams, ensure quality and post-go-live support, and drive innovation and continuous improvement.

Support hydrogeology, water resources, hydraulics, and geotechnical projects through data collection, modeling, analysis, visualization, field work, and client communication; assist with proposals, project management, and business development under supervision.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Develop and train large-scale multimodal foundation models (vision and language) that fuse imaging with molecular data, build scalable distributed ML pipelines for petabyte-scale datasets, and collaborate with experimental scientists and engineers to productionize models and systems.

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.

Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

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.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Own the design-for-manufacturing software layer of the Matter Compiler CAM stack. Develop computational geometry algorithms, manufacturability constraints, layout and retention logic, physical process models, validation systems, and production knowledge bases. Collaborate with AI, modeling, design, and process engineering teams to translate manufacturing expertise into auditable, testable code and validate results against fab runs.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Embed with enterprise customers to design, build, and deploy production agentic AI workflows. Integrate platform with customer systems, handle security and compliance reviews, drive adoption and expansion, and feed product improvements based on field signal.

Embed with enterprise customers to deploy production agentic AI workflows: integrate data sources, identity, CRMs and APIs; handle security, SSO/SCIM, isolated VMs and compliance; drive adoption, instrument usage, and feed product improvements to scale deployments.

Lead development and scaling of a customer-facing Agentic AI platform using LLMs and agents. Define roadmap, prompt strategies, evaluation frameworks, guardrails, and experiments to improve automation, accuracy, and customer satisfaction while partnering with engineering and OpenAI.

Lead consolidation and strategy for the company's AI platform: migrate distributed prompts to a unified system, prioritize chatbot work, drive architecture and API decisions, own the OpenAI relationship and ChatGPT App presence, evaluate emerging technologies (voicebot, computer capabilities), and coordinate engineering, data, and operations to deliver production-grade AI features.

Lead consolidation and technical strategy for Jerry.ai's AI platform. Own prompt migration, chatbot prioritization, vendor relationships (e.g., OpenAI/ChatGPT), and platform architecture. Coordinate engineering, data, and operations, drive execution, evaluate emerging voice/computer capabilities, and ensure the platform remains reliable while evolving to support scalable, production-grade AI agent products.

Lead development and scaling of Jerry.ai's customer-facing AI agent platform. Own roadmap, prompt strategies, evaluation frameworks, guardrails, and experimentation to improve automation, quality, and customer satisfaction. Translate between engineering and business, partner with OpenAI on voice/workflow models, and establish platform standards for production-grade AI systems.

Lead end-to-end development and scaling of Jerry.ai's agentic AI platform and customer experiences. Define roadmap, partner with engineering on prompts, evaluation frameworks, guardrails, and APIs. Drive experiments to improve automation, answer quality, and customer satisfaction while integrating next-gen voice models and partnering with vendors like OpenAI.

Lead development and scaling of an AI agent platform integrating LLMs, agents, and internal APIs. Define roadmaps, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI to improve automation, answer quality, and customer experience through experimentation and production deployments.

Lead end-to-end development and scaling of Jerry.ai's AI agent platform, unifying LLMs, prompt strategies, human-in-the-loop workflows, and APIs to increase automation and customer satisfaction. Partner with engineering, product, and OpenAI to design evaluation frameworks, guardrails, and rollouts; run experiments to improve quality, latency, cost, and automation rates.