AI Product Manager

AI Product Manager (Technical) - Santa Barbara CA (Onsite)

Mandatory Requirements:

4 years of experience in product management or a related technical role shipping solutions to real customers.

Experience as an individual contributor not a manager of PMs.

Previously worked as a software engineer before moving into product.

Experience operating in early-stage or startup environments.

We are looking for an AI Product Manager with 4 years of experience to be the first PM on our AI team. This person will take ownership of the companys core product which analyzes large-scale customer feedback and generates actionable insights for companies like DoorDash Stripe and Lululemon. This is a highly technical hands-on role that sits at the intersection of product data and AI systems.

You will define what to build how to build it and how to measure its success taking over a core function currently owned by one of the co-founders. We are looking for someone who is both deeply technical and highly customer-facing capable of independently engaging with senior stakeholders shaping product direction and getting into the weeds to validate and build new AI capabilities.

What will you be doing

• Own the product strategy for the companys core insights platform defining how the product evolves over the next 612 months and identifying where current insights are strong vs. where they need to improve.
• Engage directly with enterprise customers to deeply understand their workflows validate product direction and ensure the insights we generate are actionable and drive real business decisions.
• Design and execute the technical strategy for new AI features including leveraging LLMs to build and iterate on proofs-of-concept and early product experiences.
• Define how we evaluate AI quality by building datasets labeling frameworks and quantitative metrics to measure performance and implement systems to monitor and improve model output over time.
• Partner closely with engineering to take products from 01 translating ambiguous problems into concrete product and technical plans and driving them through to production.
• Drive expansion into adjacent product areas (e.g. support sales and operational insights) by identifying new opportunities validating them with customers and turning them into scalable product offerings.

Tech stack

LLMs NLP Embeddings Clustering