Founding Platform Engineer

**FIG** Founding Platform Engineer; $250K–$350K base + 20% pod profit share + founding equity; New York, NY (Hybrid)

**About Company** Foundational Insurance Group (FIG) is an AI-native, full-stack specialty insurance carrier built to operate like a quantitative hedge fund rather than a traditional insurer. FIG owns its own balance sheet and organizes around small, autonomous product pods — each pod finds an underserved or entirely new insurance market, builds a differentiated underwriting strategy on proprietary data and ML pricing models, and runs its own book and P&L once cleared through FIG's internal research and validation gates.

FIG has raised $107M in seed capital. First products are expected to go live in under 90 days, with the business targeting cash-flow positive in year one and the first five products funded off balance sheet.

Founded by Benjamin Markoff (CEO) — who built and exited Founder Shield (insurtech, nine-figure gross written premium, acquired by The Baldwin Group in 2021), the tech-enabled MGA Scale Underwriting, and Broker Buddha (exited January 2023) — and Judah Sosnick (CTO), a founding engineer at an enterprise AI/prediction-modeling startup who went on to build a systematic equities trading platform and served as CTO of an entertainment holding company deploying LLMs and agentic AI. The team will stay intentionally small (tens, not hundreds, of employees), so every hire is load-bearing.

**About the Role** This role sits above the product pods rather than inside any one of them, owning the internal underwriting operating system that every pod builds on top of — the data backbone, model serving, experimentation and backtesting, underwriting workbenches, and agentic research workflows that have to carry each new insurance opportunity through FIG's stages of conviction and validation.

Note on scope: this JD frames the role as full end-to-end ownership. On a live client call, the CEO (Benjamin) described the day-to-day closer to "helping Judah [CTO] build it" than sole ownership. Set candidate expectations as collaborative build-out under the CTO's direction, at least initially, rather than pure autonomous ownership.

**Responsibilities** - Own and build the central data backbone: ingestion, transformation, and governance across highly varied specialty-insurance data sources. - Build and operate model deployment and serving infrastructure for pricing and underwriting models. - Build product experimentation and backtesting systems that let pods validate a new insurance strategy before committing capital. - Build human and AI-assisted underwriting workbenches used directly by underwriters and pod leads. - Build agentic workflows for research, demand signaling, and distribution validation ahead of a product launch. - Build audit, governance, and compliance flows across the platform. - Sit with the CEO, CTO, and Chief Underwriting Officer to translate business requirements into technical specs, then lead execution.

**Required Skills** A. Has built a highly configurable, end-to-end internal workbench or platform before, not a customer-facing product. B. Systems and architecture background from an AI lab, big tech company, or startup building the workflows and infrastructure around ML products, not necessarily the models themselves. C. Comfortable prioritizing architecture and functionality over visual polish, since this is internal tooling. D. Enterprise-grade instincts for data-intensive, model-intensive, agent-driven systems.

**Bonus Skills** - Product management experience at a big tech company, or strong product instincts and fluency (confirmed nice-to-have, not required) — this person will take a business requirement directly from the CEO, CTO, and Chief Underwriting Officer and translate it into what gets built.

**Logistical Info** - Location: New York City, hybrid — flexible for A+ talent. Tri-State-based candidates: 2–3 days/week in-office. Candidates based elsewhere: roughly one week per month in NYC. Open to candidates relocating to the NYC area. - Compensation: $250K–$350K base + 20% profit share on the pod's profit above an agreed hurdle rate, + founding equity. Client notes a successful pod could put all-in comp in the seven-figure range. - Number of openings: 1 - **Other:** No visa sponsorship — candidates must already have US work authorization. Founding equity on this seat.

**Ideal Background** - Background at an AI lab, big tech company, or startup doing platform and systems work around ML products. - No fixed seniority requirement on paper, but in practice the client has passed on candidates as "too junior" and "not strong enough in key areas to be the first hire" even at ~3+ years (confirmed rejections, Aug 2026). Read this as: no floor on years, but a real floor on demonstrated ownership and depth. A PhD-fresh candidate needs to show founder-caliber capability, not just credentials — don't submit purely on "no seniority bar." - Example candidates the client flagged as an instant "yes": linkedin.com/in/gm5315, linkedin.com/in/sridharnimmagadda.

**Green Flags** - Curious, independent, sometimes contrarian thinker. - Ships fast without sacrificing quality; genuinely excited to build foundational systems from zero. - Comfortable translating a loosely defined business requirement from a CEO/CTO conversation into a build plan and leading it.

**Red Flags** - Needs a mature, well-scoped internal-tools org to be productive — this is a from-scratch build inside a small technical team. - Prioritizes visual polish over architecture and functionality for internal tooling. - No US work authorization and would require visa sponsorship — FIG does not sponsor.

**Interview Process** 1. Interview with Benjamin Markoff (CEO). 2. Interview with Judah Sosnick (CTO). 3. Take-home project, with a 48-hour turnaround.