Senior Product Manager

Shamrock AI

San Francisco (CA)

On-site

USD 180,000 - 220,000

Full time

14 days+

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Benefits offered by this job

Early-stage equity

Full medical, dental, vision

Learning budget

Job summary

Shamrock AI is seeking its first dedicated product manager to own the roadmap, requirements, and build order, staying close to customers, engineers, and data.

You’ll define the data-quality and governance gaps to be solved in real time, across heterogeneous systems, before the model runs, and translate needs into concrete platform features.

Qualifications

5+ years in product management with at least 2 years on an enterprise B2B product
Understanding of LLMs, RAG, evaluation frameworks, and data-quality gatekeeping
Read a data spec, spot ambiguity, translate into engineering requirements
Effective with CIOs, data engineers, and co-founders in one day
Experience with data governance, data quality, or data validation in product context
Exposure to healthcare/financial services/manufacturing sectors

Responsibilities

Own the roadmap: prioritize features based on customer value, feasibility, and strategy
Run customer discovery with CIOs, data architects, and transformation leads
Build working LLM prototypes using Claude/Lovable to gather feedback for engineers
Define AI agent layer: validation, outputs, and evaluation of success
Set and track product metrics: adoption velocity, time-to-value, validation coverage, false positives
Collaborate with FDSEs, Backend Engineers, and GTM on deployment patterns and product narrative
Make calls when engineering and customer needs conflict and own those decisions
Engage in first 90 days with customers and establish product cadence

Skills

Enterprise PM experience

AI fluency

Data instincts

Communication

Cross-functional collaboration

Education

5+ years product management, 2+ years enterprise B2B

Tools

Claude/Lovable

Job description

$180,000 - $220,000 USD + Early Stage Equity

About the Role

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.

The Problem You’ll Own:

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.

What You’ll Do:

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

You’ll also:

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

What This Is Not:

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

In Your First 90 Days:

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

Why Now:

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.

Compensation & Logistics:

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.

Requirements

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

Background That Maps Well:

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

About the Company

We’re building systems that continuously validate data and business processes across large enterprise environments. Enterprises run on multiple systems: ERP (e.g., SAP), APIs, internal tools, and data platforms (Databricks, Snowflake, Postgres). Inconsistencies in data - either from external vendors, internal processes, or data migrations break workflows. When AI is layered on top, those failures scale.

We build the layer that: