About Us
Commercial insurance is a $1.1T+ global market. It is financial infrastructure that lets businesses take risks: borrow, invest, grow, and recover when things go wrong. Yet it has never had a common data standard to make policies legible: what it costs, what it covers, and how it compares to the broader market. This distorts pricing, hides coverage gaps, and creates enormous friction for businesses that depend on insurance.
Advocate has raised an $18M seed round to build that standard and a network of companies that contribute real-time policy data on top of it.
Today, our customers include 4 of the top 20 financial institutions. These customers participate in a network with more than $8B of premiums across 70,000+ policies. Their policies are aggregated to power Advocate's real-time insurance market pricing feed, while the coverage terms of each individual policy are normalized into a common, machine-readable schema. That means lenders, brokers, carriers, and property owners can increasingly reference the same structured understanding of coverage instead of independently interpreting the same policy.
Insurance professionals use this common data layer to improve how they manage these 70,000 policies: evaluate coverage gaps, benchmark pricing, manage third-party compliance, and more. Every workflow consumes data from the network while adding to it: as our customers do more work on our platform, more data enters the network.
This creates a compounding data flywheel: more customer activity adds more transactions and structured coverage data, which strengthens our benchmarks and models. We get better as frontier AI models improve, we can structure more of the insurance market at greater depth and lower cost, accelerating the growth and value of the network.
We are still early. Our ambition is to make this data layer foundational infrastructure for commercial insurance and turn this Trillion-dollar market into the next financial ecosystem.
The team is small and the bar is high. You will work across a highly empowered team that owns the product roadmap and on problems without established playbooks: designing systems that turn unstructured insurance contracts into reliable data, building products on top of a rapidly growing proprietary dataset, and creating workflows that become more valuable as the network grows. We expect you to be high agency and in return you will have immediate impact on global problems. Learn more at advocate.app.
About the role
Advocate’s technical staff is primarily comprised of Product Engineers. Our Product Engineers are the core builders at Advocate and take a problem from a rough thesis to something in production that customers use daily.
Product Engineers are leaders at Advocate. Each Product Engineer is responsible for a squad that owns part of our platform: evals and benchmarks, agent systems, the data layer, reporting, traded products. We will place you as a leader of one of these squads, where your instincts are strongest.
We do not have Product Managers or QA. Our organization is comprised of senior, empowered Product Engineers, Product Designers and our Infra/Platform team. As such this is not a ticket-taking role. You will be handed problems, not specs, and you will be expected to have an opinion about the answer and take the solution to delivery. Alongside this radical ownership comes freedom to solve for outcomes.
What you do
- Own features end to end, from scope through build, deploy, and iterate, with unusual latitude on how they get built.
- Work across the stack when the problem requires it: interface, API surface, data model, background jobs.
- Turn dense, structured insurance data into interfaces a broker, lender, or underwriter reaches for every day.
- Define what your work needs from adjacent squads, whether that is data captured, an endpoint exposed, or a permission scoped, and raise the dependency before it becomes a blocker.
- Use AI tooling aggressively as leverage. We expect engineers here to ship at a volume that only makes sense with agents in the loop.
- Contribute to the eval and benchmarking work that decides which models we trust with customer data, and where our own harness beats a raw model call.
- Bring product judgment: push back on requirements that don't hold up, propose the simpler version, kill the feature that isn't earning its keep.
What success looks like
- 30 days: You understand the data model and the product surface, and you're shipping meaningful contributions to production.
- 90 days: You independently own features end to end, from identifying the dependency to deploying the customer-facing result.
- 6 months: You're the person who sees the gap before it slows us down, whether that is a bottleneck in the interface, a missing pipeline, or a feature the market is asking for that isn't on the roadmap yet.
Your qualifications
- 2+ years building production web applications. This role can be a fit for someone with 2 years or 15 years of experience. What matters is the ability to think about a problem and ship to completion.
- Advanced, daily use of AI tools (Codex, Claude Code, agentic workflows)
- Comfort working across the development stack
- Experience with data-heavy products: dashboards, analytics tools, pricing platforms, internal tools with real complexity
- Strong product instinct and a bias toward shipping. Desire to solve product problems.
- High autonomy in a small team. You don't need process to make progress
- Hybrid and remote working options available
Nice to have
- Exposure to insurance, financial services, or another regulated, data-dense industry
- Experience with real-time data, index, or benchmark products
- Early-stage experience where you wore several hats at once
- Interest in market structure and pricing transparency
What we offer
- Salary Range: $115,000 – $190,000 USD
- Equity / stock options, granted in addition to base salary
- Health & wellness: standard health benefits
- Work-from-home stipend
- Learning & development budget + team events
- Mentorship from industry leaders (ex-Goldman Sachs, Apollo, Shopify, Productboard, and more)
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