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Salary: GBP 40,000 - 55,000 / annual
Hyperexponential is an AI-powered insurance platform trusted by nearly 50 of the world's largest insurers. The company is backed by a16z, Highland Europe, and Battery Ventures, and is scaling globally with zero churn and billions in premiums flowing through its platform.
The People team owns systems and data that keep hx a high-performance, high-engagement workplace: performance and growth, people analytics, compensation and benefits, and HR compliance. The team is building a reporting layer that enables the business to make faster, better-evidenced decisions about its people.
In this role, you will:
- Build AI tooling that the People and Talent function runs on: agents that answer questions currently landing in inboxes, skills that draft documents written manually, and workflows that pull systems together. This is the core of the role.
- Build out the People reporting layer, including dashboards on retention, attrition, and engagement, and interpret what the numbers mean.
- Work with Talent to connect hiring and pipeline data to People data, making that view something leaders can interrogate themselves.
- Learn people processes (onboarding, payroll data integrity, performance cycles) and rebuild parts that depend on manual effort.
- Support performance review cycles in Lattice and engagement surveys, analyze outputs, and automate manual components.
- Help keep HRIS data accurate, including records, reporting, and system troubleshooting.
You will work directly with People Leaders with real scope to shape how the team works as it grows. The role offers significant autonomy to find problems and build fixes; if you need a well-defined remit and clear instruction, this may be frustrating.
REQUIREMENTS:
- Built and shipped something with AI that other people used (agent, script, tool, bot, or side project).
- Gone past the first version; you can explain how you tested it, what it got wrong with real users, and what you changed.
- Worked with more than one model or platform and formed a view on what each is good for.
- Worked with a real dataset, cleaned it up, and drawn a defensible conclusion.
- Been responsible for keeping a system or record accurate (tracker, membership list, codebase, or admin role).
- Handled something that needed to stay confidential or can demonstrate how you think about sensitive information.
- Genuine interest in how organizations work and willingness to learn a domain where detail matters and consequences land on real colleagues.
- Ability to adapt to change and ambiguity, and collaborate across diverse teams.
- Energized by a culture that values ownership, feedback, and continuous learning.
