AI Automation Senior Lead

About the role

We are looking for a builder to help lead our 'AI for Work' efforts. Together with the Director of AI Strategy and Enablement, you will build the internal AI infrastructure that makes our teams more efficient. That means building, evaluating vendors, and continuously evolving the AI systems our teams run on with the goal of maximizing every person's efficiency and scaling our ability to deploy agents across the business.

You will embed directly within business functions across the organization, deeply understand how teams operate today, and fundamentally reimagine how they should operate tomorrow. This is not about bolting automation onto existing processes; it's about redesigning how work gets done with agentic tooling transforming (and eliminating) workflows.

You will partner with leaders across the business to surface high-impact opportunities, architect intelligent solutions, and deliver measurable gains in productivity, output quality, and the experiences we deliver to our employees. You will define what "AI-native operations" looks like inside Taskrabbit.

Responsibilities

• Partner multiple functional leaders to define new standards for how teams operate in an AI-augmented environment. Reporting to the Director of AI Strategy and Enablement, you will establish the "AI-first" baseline for daily execution, ensuring that ways of working are clearly defined and consistent across all non-engineering functions.
• Conduct structured discovery, shadowing teams, interviewing stakeholders, and mapping end-to-end processes, to identify where AI can eliminate, redesign, or dramatically accelerate work.
• Embed within internal business functions to develop a deep, firsthand understanding of current workflows, pain points, and decision-making processes to map high-value process bottlenecks.
• Reimagine workflows from first principles rather than automating existing steps; challenge assumptions about what requires human involvement and what doesn't.
• Design, build, and deploy AI-powered automations, agents, and internal tools that reduce time spent on repetitive, low-value-add tasks while improving the quality and consistency of outputs.
• Integrate large language models, agentic frameworks, and automation platforms into internal workflows with appropriate guardrails, error handling, and human-in-the-loop checkpoints.
• Measure everything: establish baselines before intervention, track adoption and usage patterns post-deployment, and quantify productivity impact in terms of hours saved, error rates reduced, and cycle times compressed.
• Serve as a trusted advisor to functional leaders on what AI can and cannot do for their teams; translate business problems into technical solutions and technical constraints into business language.
• Stay abreast of the rapidly evolving AI tooling landscape (e.g., LLM capabilities, orchestration frameworks, RPA-to-AI convergence, no-code/low-code platforms) and calibrate our internal stack accordingly.
• Build reusable patterns, templates, and playbooks so that successful automations in one function can be adapted and scaled across others.
• Collaborate closely with the engineering and product organizations to ensure internal automation efforts align with our broader technology strategy and do not create shadow IT risk.
• Help to define the framework for evaluating the success and health of our workforce transformation. You will determine the key signals that demonstrate progress, partnering with Finance and Data Science to ensure that operational shifts deliver validated value to Taskrabbit.
• Focus on moving beyond tool adoption to fundamental change, ensuring our teams are positioned to focus on high-value, high-judgment work that drives enterprise performance.
• Beyond individual projects, focus on helping to build the mindset and capability for teams to lead their own evolution. You will create the models that allow the organization to stay agile, ensuring the workforce can continuously adapt as technology and market conditions change.

Qualifications

• Education: A Bachelor’s degree in Computer Science or a comparable technical field, with demonstrated interest in Business Analytics, Finance, Data Science, applied AI, or related disciplines.
• Experience: 5+ years of professional experience, including 1–2 years of direct, relevant experience in AI, data, or analytics-driven roles.
• GenAI Fluency: Hands-on experience with generative AI tools (e.g., Gemini, ChatGPT, Claude) through professional, academic, or personal projects, with a strong understanding of concepts such as state management and prompt versioning.
• AI & ML Knowledge: Solid understanding of AI technologies and machine learning concepts, and can apply them effectively in business contexts.
• Analytical Thinking: Strong analytical mindset, with the ability to design experiments, interpret metrics, and distinguish meaningful signals from noise.
• Communication Skills: Clear and concise communication, translating complex technical insights into actionable recommendations for non-technical stakeholders.
• Ownership & Drive: Curiosity, bias for action, and strong sense of ownership in fast-moving, ambiguous environments.
• Hands-on Builder Mindset: Scrappy, execution-oriented builder who can take ideas from stakeholders and independently develop working agentic workflows without relying on external engineering resources.
• Business Acumen: Translate complex AI capabilities into clear business value, articulating ROI and impact for diverse stakeholders.
• Operational Excellence: Rigor in operations, including managing vendor relationships, forecasting technical costs (e.g., token usage), and maintaining system reliability and SLAs.
• Strategic AI Perspective: Proactive identification of implications for business and workforce transformation, bridging the gap between what is technically possible and what is practically scalable.
• Leadership & Influence: Self-starter who thrives in ambiguity, leading high-stakes discussions with senior leaders, using structured thinking and compelling storytelling to drive alignment and change.
• Impact Orientation: Deeply data-driven, partnering effectively with Finance and Data Science to define success metrics and ensure initiatives deliver measurable productivity gains and business impact.
• Continuous Learning: Strong intellectual curiosity and commitment to continuous learning, staying ahead of emerging technologies to ensure long-term organizational readiness.