СШАSenior Product Manager, AI Pod
1 нед. назад
170k–210k USD / годСШАSeniorHybrid
aidigital workflows
Senior Product Manager role at focusing on AI-powered insurance advisory tools to improve client experience and operational scalability.
О компании
- is the insurance broker that powers ambition.
- We’re a tech-enabled insurance advisory and brokerage purpose-built for growing companies in technology, life sciences, and professional services. Our clients are ambitious leaders building complex businesses, and we help them manage risk with tailored advice, smart coverage, and responsive service.
- Backed by over $200M from world-class investors, combines deep industry expertise with AI-powered tools to deliver a better insurance experience. Our digital workflows reduce friction, speed up decisions, and give our clients the confidence to move faster.
- Not only is this an exciting and growing team where you can drive a real impact on our operational scalability, but is also the preferred insurance provider to customers of Y Combinator, Brex, Carta, and WeWork. We’re a quickly growing startup that believes in transparency and acknowledgment with our team members and cultivating a values-driven company. Our values are "Be Client Obsessed", "Own it together", "Act with integrity and empathy", "Stay Curious and Grow", and "Empower People."
Обязанности
- 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.
- You'll be the founding partner on the pod embedded in a team of AI and software engineers. This isn't a conventional PM role. You're the person who owns the seam between humans and the system, runs a parallel build track so the pod isn't single-threaded on engineering, and holds us accountable to whether the engine is actually learning.
- 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.
- Sit alongside brokers. Watch them work. Try interaction patterns. Discover what "human in the loop" actually means in practice — not as a phrase, as a working interaction pattern.
- Ensure the team is solving the right problem. You are the interface between what brokers need and what the system can produce.
- Map workflows into tangible assets, advice units, and recommendations for brokers to act on.
- 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.
- Stand up solutions brokers can touch by mid-week. Without blocking on the Lead AI Engineer. You'll use Claude Code, Cursor, Lovable, v0, or whatever gets there fastest.
- Run the learning backlog. Track what we've learned about the engine, what we still don't know, and whether each iteration is closing the gap. This is interpretive work — reading instrumentation, forming hypotheses, feeding them into the next cycle.
- 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.
- Author the metric system — north-star, driver, and guardrail metrics tied to unit economics (revenue per FTE, cost per case) — and hold the team accountable to it.
- Recruit and run the pilot. Pick the first users yourself, sell them on it, walk each one through the system 1:1, and convert their feedback into the next build cycle. Adoption is yours to earn, not GTM's to deliver.
Требования
- You've shipped AI products in production. Not pilots. Not evaluations. Real systems with real users — including at least one with a feedback loop where human corrections improved the system over time.
- You build. You have side projects. You have artifacts you've shipped yourself. You can walk us through code you've personally written. Your daily workflow includes Claude Code, Cursor, Lovable, v0, Replit, or similar.
- 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
- You've done 0-to-1. You've defined success criteria for things that have never been measured. You've shipped before requirements were clear. You found it energizing.
- Field research is a reflex. Dropped into a domain you don't know, your first move is to sit with the people who do the work — think ten practitioners shadowed and interviewed in your first two weeks, and an artifact they rate worth using by week three.
- 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.
- You operate in writing. TL;DR-first updates, standup recaps that read as decision logs, and status reporting that separates shipped from in-flight from scheduled — without inflating any of it. You can articulate technical tradeoffs to non-technical stakeholders and hold your own in architecture conversations with engineers.
- You have a real frontend foundation. Comfortable shipping working frontend prototypes yourself. You don't need to be a production engineer — you need to be someone who's built and deployed at least one app, can read and modify code, and reaches for AI dev tools to stand something up by mid-week. Your FE foundation might come from undergrad CS classes, a bootcamp, a one-off course, an early-startup role where you wore the hat, or self-taught side projects. Recency matters less than whether the foundation is real.
- You run the analysis yourself. Given a book of business and a segmentation question, you can pull the data (SQL/Snowflake), quantify the options, and come back with a defensible cut — numbers, caveats, and a named override rule. You know how to distinguish signal from noise in both qualitative feedback and quantitative instrumentation.
- 💰 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 membership.
- 📊 Regular performance reviews: conducts regular performance discussions with all team members, offering goal setting and check-ins, development discussions, and promotion opportunities.
Будет плюсом
- 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.
- Experience instrumenting products end to end — defining events, standing up dashboards, and closing the loop from telemetry back into the roadmap.
- Prior startup experience, or experience inside a company operating in a startup mode — where shipping, learning, and pivoting happened in weeks, not quarters.
Условия
- Our salary ranges are based on paying competitively for our size and industry and are part of our total compensation package, which also includes benefits and other perks. We also include stock options in all compensation packages and believe all employees should have the opportunity to become owners in the company. Individual pay decisions are based on a number of factors, including qualifications for the role, experience level, skill set, location, and business need. The pay range provided is subject to change and may be modified in the future.
- believes in putting our people first, and building a diverse team is at the front of everything we do. We welcome people from different backgrounds, experiences, perspectives, and ranges of abilities. We are an equal-opportunity employer and celebrate the diversity of our growing team.
Как откликнуться
- (Please note these steps may vary slightly depending on the role)
- 30-minute phone call with our recruiting team
- 30-45 minute video interview with the hiring manager
- Case study/technical screen
- Meet the team! 30-45 min 1:1 video discussion with 3-4 team members you’d work closely with in the role
- Executive chat
Другое
- has employees located across the U.S., with offices in San Francisco, Chicago, and New York City. This role is aligned to our Chicago office. While this role has hybrid work flexibility, we require team members to be in the office at least three days per week (Tuesday, Wednesday and Thursday) to foster close collaboration and team building.
- A feature PM role — there is no inherited backlog, roadmap, or ceremony. You'll mint the team's operating rhythm yourself — standups that end in decision logs, milestone scoping, a written cadence — and run it.
- A stakeholder-appeasement role — you'll recruit pilot users, run the rollout, and own whether the field adopts what you ship, but the output is measured system performance, not stakeholder satisfaction.
- A role with fully defined success criteria — you'll help build those from scratch, and you should find that energizing.
- A role where you need to already know insurance — you need to be excellent at learning complex domains fast.