Research Engineer - AI Team

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Until now, insurance has been making billion-pound decisions using outdated tools.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 hxAI team

The Research Engine sits at the intersection of exploration and evidence-based decision-making. We ensure that major technical investments are de-risked through rigorous research before Engineering scales them into production. Our work shapes what gets built, how it's built, and whether it's worth building at all.

As a Research Engineer, you'll bridge Product Management and Engineering by investigating emerging AI capabilities, validating technical approaches, and producing evidence that guides strategic product decisions. You'll design evaluation frameworks that measure agent performance consistently, run structured experiments to test competing hypotheses, and translate complex findings into clear recommendations that influence roadmap priorities and implementation choices.

This isn't abstract research; it's applied investigation with direct commercial impact. Your work accelerates Engineering delivery by reducing uncertainty upfront, strengthens product quality by identifying failure modes early, and builds institutional knowledge about what works in one of AI's most complex application domains: specialty insurance pricing and underwriting.

Check out our AI Candidate Hub for a behind the scenes look at the team you would be joining!

What you'll be doing

• Design and maintain evaluation frameworks that enable consistent, automated measurement of AI agent performance across hundreds of insurance scenarios, creating the testing infrastructure that catches regressions before customers do
• Conduct structured technology assessments that evaluate emerging AI capabilities against specific product needs, producing technical briefs and competitive analyses that inform roadmap decisions worth millions in engineering investment
• Run disciplined experiments that decompose ambiguous research questions into testable hypotheses, using statistical rigour and practical engineering insight to generate reliable findings under tight delivery timelines
• Build comprehensive test suites covering actuarial edge cases, underwriting workflows, and pricing logic that stress-test AI systems in ways that mirror real customer usage, improving agent reliability by 40%+ through targeted improvements
• Translate research findings into engineering action by identifying performance gaps, recommending technical approaches, and working alongside Product and Engineering to turn insights into shipped capabilities customers trust
• Maintain domain fluency in insurance by staying current with actuarial concepts, underwriting practices, and pricing methodologies, ensuring research outputs respect the complexity of the industry we're transforming

What you'll need to have done

(or have some intuition on):

• Worked on applied AI or ML problems where you had to structure your thinking, test hypotheses, and draw conclusions from messy or incomplete data - doesn't need to be formal research, but you should be able to show how you approached ambiguity methodically
• Had some exposure to evaluating AI or ML systems - whether that's writing test suites, tracking model performance metrics, or thinking critically about why a model fails in certain scenarios. We're not looking for a fully-formed eval framework, but you should understand why this matters
• Worked in or alongside production AI systems