Staff Algorithm Engineer

• Master’s or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a highly quantitative field, or equivalent commercial experience. • Background in designing, building, and deploying complex machine learning models in production environments. • Strong software engineering skills with deep proficiency in Python and libraries like NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow. • Working knowledge of cloud platforms (specifically AWS) and containerization technologies (such as Docker and Kubernetes). • Excellent communication skills, enabling you to explain complex technical concepts clearly to cross-functional stakeholders. • Ability to mentor engineers and establish team-wide best practices for software engineering, model development, and code quality. • Commercial acumen, with an understanding of the insurance or financial services industry (highly desirable).

Who are we?👋 Look at the latest headlines and you will see something Ki insures. Think space shuttles, world tours, wind farms, and even footballers’ legs.  Ki’s mission is simple. Digitally disrupt and revolutionise a 335-year-old market. Working with Google and UCL, Ki has created a platform that uses algorithms, machine learning and large language models to give insurance brokers quotes in seconds, rather than days.  Ki is proudly the biggest global algorithmic insurance carrier. It is the fastest growing syndicate in the Lloyd's of London market, and the first ever to make $100m in profit in 3 years.  Ki’s teams have varied backgrounds and work together in an agile, cross-functional way to build the very best experience for its customers. Ki has big ambitions but needs more excellent minds to challenge the status-quo and help it reach new horizons. Where you come in? We’re looking for a Staff Algorithm Engineer to lead the design, implementation, and optimization of the algorithmic models powering the Ki platform. You’ll drive the technical development of our machine learning systems, researching and applying advanced mathematical and statistical techniques. In this role, you’ll collaborate closely with data scientists, software engineers, and underwriters to integrate these models seamlessly into production. Your work will have a significant and direct impact on our underwriting performance, efficiency, and risk selection. You’ll own projects spanning from short-term model updates to long-term architectural designs that shape our technical future. What you will be doing: 🖋️ • Algorithmic Model Development: Design, build, and maintain production-grade machine learning and statistical models to automate risk assessment and underwriting decisions. • Systems Architecture and Engineering: Architect and build robust, scalable, and secure data pipelines and infrastructure to support model training, deployment, and monitoring. • Collaboration and Integration: Collaborate closely with Data Scientists, Software Engineers, and Underwriters to integrate models into our core platform and ensure high availability and performance. • Technical Leadership: Provide mentorship and guidance to other engineers on our team, establishing best practices for software engineering, model development, and code quality. • Research and Innovation: Stay up-to-date with the latest advancements in AI, machine learning, and statistical modelling, and evaluate their potential application to Ki's business. • Master’s or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a highly quantitative field, or equivalent commercial experience. • Background in designing, building, and deploying complex machine learning models in production environments. • Strong software engineering skills with deep proficiency in Python and libraries like NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow. • Working knowledge of cloud platforms (specifically AWS) and containerization technologies (such as Docker and Kubernetes). • Excellent communication skills, enabling you to explain complex technical concepts clearly to cross-functional stakeholders. • Ability to mentor engineers and establish team-wide best practices for software engineering, model development, and code quality. • Commercial acumen, with an understanding of the insurance or financial services industry (highly desirable).