Senior AI Platform Engineer

About hyperexponential (hx) At hyperexponential, we’re building the AI-powered platform that enables the world’s most critical decisions in a $7 trillion industry, which risks to take, and how to price them. These are the decisions that shape real-world outcomes: whether rockets successfully launch into space, autonomous vehicles make it to market, or communities recover after major storms. Until now, insurance has been making billion-dollar decisions using outdated tools. We’re changing that. Our platform brings together data, AI, and human expertise to give insurers the fastest path from submission to decision - helping them move faster, act smarter, and take on more risk with confidence. Backed by a16z, Highland Europe, and Battery Ventures, we’re scaling globally - already trusted by nearly 50 of the world’s largest insurers, with zero churn and billions in premiums flowing through hx. What began as a single product in one market has rapidly evolved into a multi-product, multi-territory platform powering every stage of pricing and underwriting. AI is at the core of what we do - from building the world’s first domain-specific AI peer programmer for insurance (think GitHub Copilot with a PhD in actuarial science) to shaping agentic workflows that reinvent how this industry operates. What makes hx different is the people who build it. Here, impact isn’t tied to title or tenure; it’s defined by the challenges you take on and the discipline you bring. Surrounded by peers who stretch you, you’ll do the best, hardest work of your life in a company engineered to endure. If that sounds like you, join us in building what comes next. About the AI Platform team At hx, AI agents generative AI are becoming embedded across every team and product. The AI Platform team exists to make that happen safely and at scale. We own the shared building blocks that enable any technology team at hx to build, ship, and operate AI agents quickly, safely, and consistently. We build the platform, not the agents. The reusable foundations that agent-building teams depend on: observability for end-to-end tracing, cost tracking, and visibility across all AI usage; gateways for unified LLM access and centralised tool routing; governance for agent identity, authorisation, cost controls, and audit logging; and developer experience through SDKs, documentation, and patterns that make spinning up a production-grade agent fast and consistent. We are a small, high-ownership team that moves fast and builds for adoption. We measure success not by what we ship, but by what other teams can build because of what we ship. What you'll be doing Observability & Evaluation : Building and evolving the platform that gives hx end-to-end visibility into AI agent behaviour and quality. Tracing, cost tracking, usage dashboards, and shared evaluation infrastructure that helps teams measure and improve agent performance. Composition : Designing and operating the infrastructure that connects agents to LLMs and tools. Unified proxies, centralised tool routing, discovery, and access governance so agent-building teams don't have to solve these problems individually. Governance : Defining and implementing the identity, authorisation, and policy layer for AI agents. Scoped credentials, per-tool permissions, audit logging, cost controls, and trust boundary patterns that let agents operate safely as they scale. Developer experience : Shaping the tools, SDKs, documentation, and patterns that make it fast for any team to go from zero to a production-grade agent on the platform. Adoption : Driving adoption of shared platform services across technology teams, replacing bespoke per-agent integrations with consistent, well-documented capabilities. Measuring impact and iterating based on what teams actually need. What you'll need to have done Built and operated platform infrastructure used by multiple teams : API gateways, proxies, observability systems, CI/CD pipelines, cloud infrastructure, or developer platforms where adoption and reliability were your responsibility. Delivered developer-facing tools, SDKs, or APIs that other engineers relied on daily, with a strong focus on usability, documentation, and developer experience. Worked with modern AI infrastructure : LLM integrations, agent frameworks, or AI-related tooling. You don't need to be an ML researcher, but you should have hands-on experience building with AI — whether in production, meaningful side projects, or open source — and be comfortable navigating the AI tooling landscape. Operated systems with governance, security, or compliance requirements : identity models, access control, audit logging, or similar concerns where getting it wrong has real consequences. Successfully driven adoption of shared services across engineering teams, replacing fragmented, team-specific solutions with consistent platform capabilities, and measuring the impact. Taken full ownership of initiatives end-to-end : from scoping and planning