AI Automation Senior Lead

Taskrabbit•San Francisco, CA

18h•Hybrid

About The Position

Taskrabbit is seeking an AI Automation Senior Lead to spearhead its 'AI for Work' initiatives. This role involves building internal AI infrastructure to enhance team efficiency, evaluating vendors, and evolving AI systems to maximize individual productivity and scale agent deployment across the business. The lead will embed within business functions to understand current operations, redesign workflows with agentic tooling, and partner with leaders to identify high-impact opportunities. This position is crucial for defining 'AI-native operations' at Taskrabbit and involves scouting, evaluating, testing, and piloting third-party AI tools. The role requires a hands-on builder who can translate business complexity into streamlined, automated solutions, focusing on both individual projects and fostering a culture of continuous adaptation and capability building within the workforce.

Requirements

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.
5+ years of professional experience, including 1–2 years of direct, relevant experience in AI, data, or analytics-driven roles.
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.
Solid understanding of AI technologies and machine learning concepts, and ability to apply them effectively in business contexts.
Strong analytical mindset, with the ability to design experiments, interpret metrics, and distinguish meaningful signals from noise.
Clear and concise communication skills, translating complex technical insights into actionable recommendations for non-technical stakeholders.
Curiosity, a bias for action, and a strong sense of ownership in fast-moving, ambiguous environments.
Scrappy, execution-oriented builder who can take ideas from stakeholders and independently develop working agentic workflows without relying on external engineering resources.
Ability to translate complex AI capabilities into clear business value, articulating ROI and impact for diverse stakeholders.
Rigor in operations, including managing vendor relationships, forecasting technical costs (e.g., token usage), and maintaining system reliability and SLAs.
Proactive identification of implications for business and workforce transformation based on rapid advancements in AI (e.g., LLMs, agents). Ability to bridge the gap between what is technically possible and what is practically scalable.
Self-starter who thrives in ambiguity and can lead high-stakes discussions with senior leaders, using structured thinking and compelling storytelling to drive alignment and change.
Deeply data-driven, partnering effectively with Finance and Data Science to define success metrics and ensure initiatives deliver measurable productivity gains and business impact.
Strong intellectual curiosity and a commitment to continuous learning, staying ahead of emerging technologies to ensure long-term organizational readiness.

Responsibilities

Partner with multiple functional leaders to define new standards for how teams operate in an AI-augmented environment.
Establish the 'AI-first' baseline for daily execution, ensuring clear and consistent ways of working 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, determining key signals that demonstrate progress.
Partner 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.
Help build the mindset and capability for teams to lead their own evolution.
Create models that allow the organization to stay agile, ensuring the workforce can continuously adapt as technology and market conditions change.