Applied AI Lead

Owner.com

3d•$140,000 - $180,000•Remote

About The Position

Owner is scaling its Applied AI organization. This is a fast-paced team that works on the most valuable opportunities. As an Applied AI Lead, you will be responsible for identifying opportunities across the business where AI can have the most impact, sizing the potential, and then building, shipping, and iterating on solutions. The role involves creating efficiency by automating manual work and developing better outcomes through AI. You will decide which opportunities to prioritize. This role is 100% remote and can be based anywhere in the United States or Canada.

Requirements

A track record of finding the problems worth solving.
Root-cause instincts; digging until the problem is understood.
Hands-on experience building with LLMs, integrating models and APIs (agents and assistants, structured output, evals, context engineering).
3+ years of experience in a 0-1 operations or startup role.
Ability to move fast, work scrappy, and thrive in an ambiguous, early-stage startup environment with tight timelines.
Comfortable taking ambiguity and turning it into a ruthlessly-prioritized roadmap.
Treating shipping as the midpoint and iterating until impact is achieved.
Strong communicator who can break down complex concepts for teammates.

Nice To Haves

Restaurant industry experience is a plus!

Responsibilities

Finding opportunities across the business where AI can have the most impact.
Sizing the potential impact of AI initiatives.
Building, shipping, and iterating on AI solutions.
Identifying and prioritizing AI opportunities.
Carrying each build from diagnosis through shipping, deployment, adoption, and iteration.
Executing ambitious projects that could be standalone products.
Identifying problems and opportunities, dissecting and sizing them, and deciding what gets built next.
Working day to day with the Applied AI team and partnering with key stakeholders across various teams (BizOps, RevOps, Sales, Marketing, Growth, Product, Finance, and Recruiting).
Watching how a build performs in the real world, gathering feedback, and iterating until the impact is achieved.
Breaking down complex concepts for teammates, both human and AI.