AI Automation Senior Lead

Taskrabbit•San Francisco, NY

22h•Hybrid

About The Position

Taskrabbit is seeking an AI Automation Senior Lead to spearhead their 'AI for Work' initiatives. This role involves building internal AI infrastructure to enhance team efficiency, evaluating vendors, and evolving AI systems. The lead will embed within business functions to understand current operations and redesign them with agentic tooling, partnering with leaders to identify opportunities, architect solutions, and deliver measurable productivity gains. This position is responsible for defining 'AI-native operations' at Taskrabbit and scouting, evaluating, testing, and piloting third-party AI tools. The role requires mapping process bottlenecks, identifying AI solutions, creating experiments, and supporting the implementation of cutting-edge AI technology. The lead will also be responsible for identifying efficiency opportunities and building a centralized orchestration layer for enterprise AI spend, ensuring governance, consistency, and scalability. This is a high-impact individual contributor role for a builder who thrives on streamlining complexity through automation.

Requirements

Bachelor’s degree in Computer Science or a comparable technical field.
Demonstrated interest in Business Analytics, Finance, Data Science, applied AI, or related disciplines.
5+ years of professional experience.
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) 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 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 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.
Proactively identify implications of rapid advancements in AI (e.g., LLMs, agents) for business and workforce transformation.
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.

Nice To Haves

Experience with RPA-to-AI convergence.
Experience with no-code/low-code platforms.

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, including shadowing teams, interviewing stakeholders, and mapping end-to-end processes, to identify where AI can eliminate, redesign, or accelerate work.
Embed within internal business functions to understand current workflows, pain points, and decision-making processes to map high-value process bottlenecks.
Reimagine workflows from first principles rather than automating existing steps, challenging assumptions about human involvement.
Design, build, and deploy AI-powered automations, agents, and internal tools to reduce time spent on repetitive tasks and improve output quality and consistency.
Integrate large language models, agentic frameworks, and automation platforms into internal workflows with appropriate guardrails, error handling, and human-in-the-loop checkpoints.
Establish baselines, track adoption and usage patterns, and quantify productivity impact (hours saved, error rates reduced, cycle times compressed).
Serve as a trusted advisor to functional leaders on AI capabilities and limitations, translating business problems into technical solutions and vice versa.
Stay abreast of the evolving AI tooling landscape and calibrate the internal stack accordingly.
Build reusable patterns, templates, and playbooks for scaling successful automations across functions.
Collaborate with engineering and product organizations to align internal automation efforts with broader technology strategy and avoid shadow IT risk.
Define the framework for evaluating the success and health of workforce transformation, identifying key signals for progress.
Partner with Finance and Data Science to ensure operational shifts deliver validated value.
Focus on fundamental change beyond tool adoption, positioning teams to focus on high-value, high-judgment work.
Build the mindset and capability for teams to lead their own evolution.
Create models for organizational agility, ensuring the workforce can adapt to changing technology and market conditions.