5+ years of experience in scale-focused roles, preferably in InsureTech or FinTech.
Proven ability to ship meaningful work using AI engineering tools like Claude Code or Codex.
Experience managing a data or API vendor stack and measuring quality metrics.
Strong coding skills, with experience in Python or TypeScript.
Ability to conduct evaluations related to LLM and retrieval systems, including precision and recall analysis.
Responsibilities
Manage the vendor portfolio, assessing cost, hit rates, and quality metrics.
Build a monitoring agent to track vendor performance and quality regressions.
Stay updated on AI search and retrieval options, making data-driven evaluations.
Define and sequence the enrichment roadmap, prioritizing impactful signals.
Execute end-to-end product management, contributing to coding and data analysis.
Benefits
Opportunity to directly influence cutting-edge AI data solutions.
Collaborative environment where product and engineering teams merge roles.
Exposure to the latest tools in AI and search technologies.
Flexibility and agility in execution and strategy.
High accountability and visible impact in a growth-stage company.
Full Job Description
MGT's AI Data Platform is the engine for our Agentic Underwriting: the vendor data, AI web search, extraction, and scoring that runs behind every quote MGT produces. We're hiring a Senior Product Manager to wake up every day thinking about driving quality results and expanding our datasets in tight coordination with the Insurance product team. You own and deliver the vendor stack, the quality and hit rate, and telemetry and evals to prove it.
This is not a roadmap-and-jira role. Product and engineering lanes are merging at MGT, and product talent operates fluidly across strategy, data, design, and engineering. PMs that succeed at MGT embrace end-to-end execution and lean into our comparative advantage: agility. You will write compelling PRDs and then execute, directly contributing code, analyzing data in our warehouse, and own results from concept through to post go live.
What You Own
The vendor portfolio. Manage our data and AI web search vendors as a portfolio: cost per successful enrichment, hit rate, field-level quality, latency, and contract terms. Onboard new vendors, run bake-offs with real evals, and kill or renegotiate underperformers on the numbers.
Quality and telemetry at scale. Instrument every vendor call end to end. Build (yourself, in Python/TypeScript) an agent that monitors hit rate, drift, outages, and quality regressions across the pipeline and flags them before ops or underwriting notice.
The frontier of AI search and extraction. Stay current on AI web search and retrieval options (native model search tools, search APIs, crawlers, structured extraction) and know which one wins for which entity type and why, with evals to back it.
The enrichment roadmap. Decide which new signals move loss ratio, bind rate, or underwriting speed, and sequence them. Make crisp build-vs-rent calls and defend the unit economics.
What Great Looks Like
Every vendor is measured with hit rate, quality, latency, and cost per successful enrichment, so that you can leverage those metrics to make renewal decisions.
A monitoring agent you built catches vendor degradation and pipeline drift automatically; incidents are found by the system, not by our underwriters.
You can name the best AI search option for each of our core entity types and show the eval results that prove it.
Enrichment quality improvements you shipped are traceable to a measurable change in underwriting speed or accuracy.
Minimum Requirements
You have shipped meaningful work (beyond simple prototypes) with an AI engineering tool (Claude Code, Codex, Devin, etc.) and can show the commits.
You have 5+ years of experience at a firm that achieved scale, ideally InsureTech, FinTech, or Consulting.
You build things directly. Nobody is on an island at MGT, but everyone ships.
Ideal Candidates
Evals are your native language. You've built evaluation harnesses for LLM or retrieval systems and can talk about precision, recall, and cost tradeoffs.
You've run a data or API vendor stack. You have owned third-party data integrations, measured their quality, and made the call to add or remove one.
Engineering background. You wrote code the old fashioned way pre-AI, and are using the latest tools to increase your impact.
Curiosity about the AI search landscape. You have opinions, backed by experiments, on the current AI web search and extraction options.
Growth stage experience. MGT is a small, high-accountability organization and that's a plus for you.
References from peer department managers (Engineering, Data, Underwriting) that loved working with you.
Compensation
$190,000 - $300,000 annual base salary, plus bonus and equity.
We recognize that no candidate will meet every qualification and encourage individuals with relevant experience and interest in the role to apply.
Please note: MGT does not accept unsolicited resumes from staffing vendors, including recruitment agencies and/or search firms. Please do not forward resumes to our jobs alias, MGT employees, or any other company location. Any submittals without a prior signed agreement will become property of MGT.
