Senior Product Manager, Platform (AI)

Vouch Insurance

About The Position

We're building an AI system that learns from how domain experts make decisions — and gets measurably better over time. The challenge isn't building another chatbot or copilot. It's designing a system that can extract structured intelligence from messy, real-world professional workflows, identify reliable patterns across many decisions, and surface that knowledge to the point where it's actually useful — without requiring anyone to change how they work. The PM is responsible for designing the loops that capture judgement, play back recommendations and advice to brokers, and holds the team accountable to a standard of truth over confirmation. This is a founding role on a small pod focused on a narrow problem space with a large solution space. We're selecting for ambiguity tolerance, iteration speed, and range over process discipline. The tradeoff is explicit: we accept messiness in exchange for faster learning. The PM works embedded in a team of AI and software engineers. You'll report to the Director of Product and interface regularly with the leadership team as the voice of the pod — communicating progress, surfacing blockers, and translating system performance into strategic implications. You'll own the problem and success definition — not the feature list. This role requires a high-ownership mindset: you don't just prioritize what's asked — you define the workflow, what use case and workflow makes the most sense to prioritize to prove out the system, and what the system needs to prove at every stage to answer “is this actually working” This is not a traditional PM role. There are no customers in the conventional sense. There's a thesis, a set of domain experts who are your ground truth, and an AI system that either outperforms a generic model or doesn't. You own the bar.

Requirements

3–5 years of product experience, including meaningful time in environments where the "right" design wasn't obvious upfront — ideally 0-to-1 or early-stage product work.
Hands-on experience with deployed AI systems — not experimentation or exploration, but systems that shipped and operated in production. You understand where these systems are strong and where they fail.
Systems thinking: you design workflows, feedback loops, not feature sets. You've thought seriously about how systems learn and improve over time, not just how they process inputs. You connect the dots across systems and workflows.
Experience in regulated or complex professional domains — insurance, fintech, legal, healthcare, or similar fields where the signal is human judgment and the stakes of being wrong are real.
Comfort with ambiguity: you can define and make progress on a problem when the requirements are incomplete and success metrics don't yet exist.
Strong written and verbal communication — you can articulate technical tradeoffs to non-technical stakeholders and hold your own in architecture conversations with engineers.
An analytical mindset: you know how to distinguish signal from noise in both qualitative feedback and quantitative instrumentation.

Nice To Haves

Experience working with LLM-based systems — extraction pipelines, retrieval architectures, structured generation, or confidence calibration.
Background in knowledge management, expert systems, or any domain where the core challenge was capturing and systematizing human judgment.
Familiarity with data platforms (SQL, Snowflake) — you don't need to write the queries, but you should be able to read them.
Prior startup experience, or experience inside a company operating in a startup mode — where shipping, learning, and pivoting happened in weeks, not quarters.

Responsibilities

Design the judgment capture system
Define what broker judgment looks like when it's captured correctly — the artifact schema that turns unstructured decisions into structured intelligence.
Design the feedback loops that allow domain experts to validate, correct, and enrich what the system surfaces — without adding friction to how they already work.
Own the broker relationship: understand what advice actually looks like, what experts trust, and what the system gets wrong in interesting ways.
Map workflows into tangible assets, advice units, and recommendations for brokers to act on.
Translate expert corrections into system improvements. When brokers tell you the output is wrong, that's your most valuable signal — make sure the team acts on it.
Own the evaluation framework: what does it mean for this system to be materially better than a generic AI model with the same context?
Define success criteria for each phase — not in business metrics, but in system performance. Does the extraction work? Do brokers trust the patterns? Is month 8 better than month 5?
Build instrumentation with the engineering team to track whether the system's intelligence is actually improving. If it isn't, the answer is stop — and you should find that useful, not threatening.
Distinguish real learning from noise. Patterns that don't hold are not product failures — they're data. Own how the team uses them.
Translate domain complexity into a prioritization framework the team can execute against — without perfectly defined requirements.
Work with GTM and embedded domain experts to ensure the team is solving the right problem. You are the interface between what brokers need and what the system can produce.
Remove ambiguity without adding process. This team moves fast — your job is to create clarity, not overhead.

Benefits

Competitive compensation and equity packages
Health, dental, and vision insurance
Parental leave
Flexible vacation time
Wellness allowance
Technology allowance
Company-sponsored personal and professional development
L&D: Partnerships with Ethena and monthly Lunch & Learns
Wellbeing: access to many wellbeing perks, including Peloton, Fetch, OneMedical, Headspace care+, etc.
Caregiver Support: company seed into the dependent care FSA and company sponsored Care.com membership.
Regular performance reviews: Vouch conducts regular performance discussions with all team members, offering goal setting and check-ins, development discussions, and promotion opportunities.