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Until now, insurance has been making billion-pound decisions using outdated tools.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.
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!
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
(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 - enough to understand the gap between a prototype that works in a notebook and something customers actually depend on. Direct production experience is a plus, but strong intuition from adjacent work counts
Communicated technical findings clearly to non-technical audiences - whether that's a product manager, a stakeholder, or a customer. You don't need a track record of boardroom presentations, but you should be comfortable translating what you found into what it means
Written Python comfortably across the AI/ML stack - data processing, model evaluation, experimentation. You don't need to be a research ML expert, but you should feel at home in the tools
Been part of cross-functional teams where research or analysis fed into real product or engineering decisions - comfortable sitting at the boundary between exploration and execution, even if you're still developing confidence there
You prefer pure research environments with long timelines and academic publication goals, rather than applied investigation where findings must land quickly and influence real product decisions
You need clearly defined problems with established methodologies, rather than designing your own evaluation approaches for AI systems operating in novel, complex domains
You view research as separate from implementation, rather than seeing your work as directly enabling Engineering teams to ship higher-quality capabilities faster
If reading our Culture Document leaves you feeling neutral rather than energised, hx may not be the place where you'll do your best work. We're building something that asks for commitment and conviction, and we want you to feel excited by the opportunity to grow with us.
At hx, we're committed to salary transparency. You'll always have clarity on pay early in the process - our Talent Partner will share details with you during initial conversations - and we're working towards publishing salary information for all roles globally.
Because we're building at the intersection of technology/SaaS and insurance, our roles don't always map neatly onto traditional benchmarks. Our approach is to design compensation that's competitive in the market, fair across teams, and aligned with the impact our people make.
Equity: We offer equity across all roles at hx, making it a significant component of total compensation. Your Talent Partner will be able to share more details about this.
£5,000 training and conference budget for individual and group development.
25 days of holiday plus 8 bank holidays (33 days total).
Company pension scheme via Penfold.
Mental health support and therapy via Spectrum.life.
Individual wellbeing allowance via Juno.
Private healthcare insurance through AXA.
Income protection and Life Insurance.
Cycle to Work Scheme.
Additional perks
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