ENGINEERING AT HYPEREXPONENTIAL

At hyperexponential (hx) we are transforming the traditional insurance industry with our groundbreaking SaaS platform, hx Renew. Insurers use it to build complex mathematical models that enable smarter, faster pricing decisions—whether it’s calculating the cost to insure a global superstar’s concert tour or protecting entire states from natural disasters.
With hx Renew, insurers can rapidly develop and iterate pricing models in Python and React within a modern software environment, free from the burden of legacy tech. What once took months now takes weeks—or even days. This agility has made our product the go-to platform for insurers worldwide.
In just five years, our winning formula has earned the trust of industry giants like Aviva and Top 5 US insurers, as well as cutting-edge innovators like Convex and Inigo. Our engineering team—made up of some of the brightest (and kindest!) minds in the industry—pushes technological boundaries every day through creative innovation and regular hackathons.

REQUIRED SKILLS AND EXPERIENCE:

• Proven experience in development of large production applications with major ML components.
• Experience deploying ML models and LLMs in cloud and local environments.
• Strong programming skills in Python including its ecosystem for AI/ML, including libraries like PyTorch, Hugging Face Transformers, scikit-learn and Tensorflow
• Extensive hands-on experience with cloud-based architectures and distributed systems, building scalable, reliable, and secure solutions.
• Demonstrated ability to self manage and independently identify high-value opportunities and execute them effectively.
• Excellent communication and relationship-building skills, capable of conveying complex technical concepts to both technical and non-technical audiences.
• [Nice-to-have] Proven experience productionising LLMs for NLP use-cases

• Develop & deploy enterprise grade AI applications from the ground up.
• Accelerate our AI & Research team by designing and implementing scalable infrastructure to support rapid experimentation.
• Develop internal tools to facilitate efficient dataset and evaluation results management, to streamline team’s processes and evaluations.
• Consult on technical designs, offering guidance and expertise to ensure robust architectural decisions, while proactively identifying and addressing future technical challenges.
• Build strong relationships with key stakeholders, effectively communicating technical concepts, managing expectations, and ensuring alignment with business goals and priorities.