AI Enablement Lead

Waymo•Mountain View, CA

6h•$200,000 - $247,000•Hybrid

About The Position

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Business Systems team defines and builds the business IT architecture that supports the commercial operation of Waymo's autonomous fleet. The team stands at the intersection of finance, operations and IT; through creative architecture, expertise and attention to detail, it ensures that Waymo has world-class capabilities across all its business processes. The Data Intelligence team is the strategic engine driving the democratization of AI across Waymo G&A. Operating at the unique intersection of cutting-edge Generative AI and corporate business strategy, we don't just build solutions in isolation we design, architect, and deploy intelligent, agentic systems that transform how Waymo operates. By bridging the gap between complex data and real-world business action, we empower G&A leaders (from Finance and Supply Chain to People Ops and Legal) to automate manual processes, scale operational efficiency, and make high-velocity, data-driven decisions as Waymo enters hyperscale This role follows a hybrid work schedule and reports to Data Intelligence Manager.

Requirements

BS degree in Computer Science, or a related technical field, or equivalent practical experience
8-10 years of professional experience across data science, data engineering and business intelligence disciplines.
3+ years of technical experience with a focus on building and shipping AI/ML products
Mandatory hands-on experience using GenAI tools (vibe-coding) to turn prompts into small prototypes in a constrained environment, then use that artifact to validate requirements.
Strong proficiency in Python and experience with modern AI/ML frameworks (e.g. LangChain, Vertex AI Agent Builder, TensorFlow, PyTorch) and cloud platforms
Experience with GCP, Vertex AI, A2A, MCP and ADK frameworks and their application in enterprise systems
Excellent communication skills with the ability to articulate technical AI concepts to non-technical stakeholders

Nice To Haves

Deep expertise integrating AI and LLM solutions securely with major enterprise SaaS platforms (e.g., Salesforce, SAP, Workday, ServiceNow) in a complex, global corporate environment
Experience designing advanced agentic patterns (e.g., multi-agent orchestration, complex RAG architectures, dynamic tool use) for automating multi-step, cross-platform business workflows.
Proven track record of influencing cross-functional leadership, driving technical strategy, and managing organizational change to ensure high user adoption of new AI tools across non-technical business units
Strong understanding of AI governance, enterprise data privacy standards, and security protocols (e.g., RBAC, data loss prevention) when deploying AI applications on internal corporate data
Demonstrated ability to navigate ambiguity and drive projects from conception to launch in a fast-paced environment
Experience acting as a technical multiplier—mentoring team members, setting engineering standards for AI tooling, and guiding leadership on AI investments and roadmap prioritization.

Responsibilities

Act as a domain authority in AI across G&A, mentoring team members, setting technical standards, and guiding leadership on AI investments and roadmap prioritization.
Develop and prototype solutions by building proof-of-concepts that leverage large language models (LLMs), agent frameworks, and workflow orchestration tools.
Provide technical oversight by reviewing code and prompts, guiding system design decisions, and defining evaluation methodologies and performance benchmarks.
Partner with business units to identify manual bottlenecks in Finance, HR, Sales, Supply Chain and Marketing applying AI to automate complex processes.
Oversee integration and deployment by ensuring seamless connectivity between AI agents and enterprise systems through APIs, data pipelines, and workflow platforms.