Forward Deployed Product Manager

present of AI; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

Hebbia in New York seeks a Forward Deployed Product Manager who sits with customers to identify real problems worth solving and defines what success looks like. You will drive implementation and rollout, balancing product judgment with practical constraints, and work directly with analysts and lawyers to turn complex document workflows into clear requirements.

This role emphasizes translating customer concerns into actionable features, guiding AI-driven search and summarization, and ensuring

Qualifications

• Experience identifying problems worth automating.
• Ability to translate messy workflows into clear requirements.
• Comfort working with analysts and lawyers.

Responsibilities

• Sit with customers to identify real problems and success criteria.
• Drive implementation and rollout of solutions.
• Collaborate with AI researchers to validate results.

Skills

Product judgment

Stakeholder management

Requirements translation

Cross-functional collaboration

Job description

A Forward Deployed Product Manager typically sits with customers, finds the real problem worth solving, agrees on what success looks like, then drives implementation and rollout. A strong candidate brings product judgment, comfort working directly with analysts and lawyers, and the ability to turn messy document workflows into clear requirements. For a company like Hebbia, that means understanding how finance and legal teams actually read and query large document sets.

How it fits the AI era

This role sits where AI meets real work: the model does the searching and summarizing, but someone has to decide which problems are worth automating and what a trustworthy answer looks like. The human parts are scoping, judgment about risk, and getting busy professionals to change how they work. AI makes the rollout faster but raises the bar on explaining and validating results.

How it builds AI experience

The AI experience gained here is hands on: running AI queries over real document sets, watching where outputs fail, and shaping prompts and workflows around those failures. Building it means getting fluent in retrieval and document search tools, learning to test outputs against ground truth, and practicing translating customer complaints into product changes.

Worth showing

• Workflow scoping
• Document search fluency
• Rollout planning

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Hebbia

Hebbia's Matrix lets analysts at investment firms, banks and law firms run AI queries across thousands of their own documents at once, in a spreadsheet-like view.