Senior Product Manager

This is the first dedicated PM role at Shamrock AI. You will own the roadmap, the requirements, and the decisions that determine what we build and in what order - by staying close to customers, close to engineers, and close to the data.

Enterprise AI fails quietly. A workflow runs, the model fires, the output looks plausible - and somewhere upstream a field was wrong, a feed was stale, or a business rule was never enforced.

Existing data quality and governance tools weren’t built for the AI era - where validation must happen in real time, across heterogeneous systems, before the model runs. That gap is our product. Your job is to define exactly what fills it.

• Owning the roadmap: what gets built, in what order, and why - balancing customer value, technical feasibility, and strategic positioning
• Running customer discovery with CIOs, data architects, and transformation leads to surface real problems before they become feature requests
• Build working LLM prototypes:
  Use Claude/Lovable or similar tools to build working prototypes to gather feedback and communicate requirements clearly to Engineers
• Defining the AI agent layer: how validation agents reason, what they output, and how we evaluate whether they’re working
• Setting and tracking product metrics: adoption velocity, time-to-value, validation coverage, false positive rates

• Spend quality time with the customer understanding their workflows, and requirements
• Work with FDSEs in the field to turn deployment patterns and failure modes into platform features
• Work with Backend Engineers to build foundational product features that translate across functional domains
• Partner with GTM on positioning and the product narrative for healthcare and financial services
• Make the call when engineering and customer needs conflict - and be accountable for it

• Not a project management role - you decide what gets built, not coordinate delivery
• Not a role where the roadmap comes from above - you own it
• Not for someone who hands specs to engineers and waits - you are in the room, iterating in real time
• Not for someone who needs a large team or established process - the team is small by design

• Days 1-30:
  Shadow every active customer deployment. Document the top problems our platform solves - and the top ones it doesn’t yet
• Days 31-60:
  Publish the first structured roadmap. Prioritized, reasoned, tied to customer outcomes. Make the tradeoffs explicit
• Days 61-90:
  Ship something real that a customer uses. Establish the product development cadence going forward

Enterprise AI adoption is accelerating faster than data quality is improving. Every company deploying AI on messy data needs what we’re building. The PM who joins now defines what this product becomes and how it wins.

Salary:
Competitive with Senior PM / Group PM roles at growth-stage AI companies

Equity:
Meaningful early‑stage equity grant - first PM is a foundational hire

Location:

San Francisco; travel required for customer discovery and deployments (~20-30%)

Benefits:
Full medical, dental, vision; learning budget.

This role sits at the intersection of enterprise complexity, AI systems, and data infrastructure. You need all four of these:

• Enterprise PM: 5+ years in product management with at least 2 years on an enterprise B2B product, shipping features real customers use in production
• AI Fluency:
  You understand LLMs well enough to make good product decisions - RAG, evaluation frameworks, hallucination tradeoffs, what it means to gate an AI workflow on data quality
• Data Instinct:
  You can read a data spec, spot the ambiguity, and translate it into requirements engineers can act on. Schemas, pipelines, ERP structures, API contracts are not foreign to you
• Communication:
  Effective with a data engineer, a CIO, and a co‑founder - in the same day

• PM experience at an enterprise AI, LLM platform, or data infrastructure company
• Owned an API or platform product used by enterprise developers or data teams
• Track record of shipping 0-to-1 features in a fast‑moving, resource‑constrained environment
• Familiarity with data governance, data quality, or data validation - even from the product side
• Exposure to regulated industries: healthcare, financial services, or manufacturing