# Isabella Murrell > Senior Director of AI & Engineering Location: New York, New York, United States Profile: https://flows.cv/isabellamurrell ## Work Experience ### Senior Director of AI & Engineering @ Ryght Jan 2025 – Present | New York City Metropolitan Area ### AI Software Engineer @ Ryght Jan 2024 – Present At Ryght, I work across AI, product, and engineering to design and deliver the systems that power our clinical trial matching platform. My role focuses on translating complex business requirements into scalable AI workflows, data tooling, and backend logic - directly enabling customer-facing features. - Design and implement AI-powered features including document classification, structured data extraction, and feasibility note generation. - Build internal tooling to evaluate and improve LLM outputs, supporting structured reasoning and downstream reliability. - Develop the first version of our digital twin site matching engine, integrating data from various sources to power site profiling and ranking. Named inventor on pending patent for digital twins - Forward deploy prototypes to enterprise pharma customers for validation, then work with engineering to productionise. - Act as a bridge between product and engineering - turning abstract goals into clearly scoped, high-impact deliverables. - Lead technical direction on multiple projects, ensuring engineering efforts align with business needs and long-term product strategy. - Own data onboarding and customer integration for our initial deployments, ensuring product success from both a technical and operational perspective. Promoted to Senior Director of AI & Engineering in July 2025. ### Junior Research and Sport Science Manager, Footwear Innovation @ PUMA Group Jan 2024 – Jan 2024 | Boston, Massachusetts, United States - Contributed to PUMA Fast-R Nitro Elite 3 — recognized as Time Magazine Best Inventions 2025 for achieving lab-proven 3% improvement in running economy over Nike Alphafly 3 and Adidas Pro Evo 1 - Led sprint biomechanics project with Jamaican and PUMA elite sprinters — designed data collection protocols, built unsupervised ML model to classify athlete archetypes, delivered product recommendations that directly informed sprint spike development for Olympic cycle planning ### Performance Lab Technician @ PUMA Group Jan 2022 – Jan 2024 | Boston, Massachusetts, United States - Contributed to the development of PUMA's Nitro Lab, focusing on optimizing both hardware and software infrastructure to enhance performance and efficiency. - Designed and executed protocols to quantify footwear performance, providing valuable insights to both inline and innovation development teams. - Conducted scientific research on footwear biomechanics, leveraging advanced technologies such as full-body motion capture, force instrumented treadmills, metabolic carts, inertial measurement units, and pressure insoles, in conjunction with integrated software platforms. - Analyzed data using Visual 3D and Python, distilling actionable insights to drive decision-making and innovation, effectively communicating findings to key stakeholders. - Collaborated closely with external research partners to advance the development of high-performance footwear, ensuring alignment with industry standards and market demands. - Orchestrated day-to-day data collection efforts with athletes, fostering strong collaborative relationships and ensuring accurate and reliable data acquisition. ### Research Assistant - Musculoskeletal Modeling Group @ Auckland Bioengineering Institute Jan 2021 – Jan 2022 | Auckland, New Zealand • Contributed to a research study aimed at achieving surrogate modeling with wearable sensors to estimate joint torques, muscle forces, and other related parameters. • Assisted with data collection, processing and development of the semi-supervised machine learning workflow to create custom surrogate models The purpose behind the study: The loss of mobility of the lower limb in patients with movement disorder can affect their quality of life. Understanding human gait and its effect on the surrounding soft tissue is essential to assess and monitor abnormal human motion. Traditional optical motion capture methods collect gait data in a controlled environment that only provides a small and restrictive snapshot of daily living. With the advent of wearable sensors and smartphones, we can collect human gait data outside of the clinic into the community. Our aim is to develop a novel machine learning workflow to create custom surrogate models that use wearable sensors to predict human gait properties in the real world. ### Machine Learning Intern @ IMeasureU Jan 2019 – Jan 2020 | Auckland, New Zealand • Interned at IMeasureU, primarily assisting in data collection, labeling, and testing, as well as contributing to the creation of a pole vault case study using their product. • Collaborated with the team to analyze data trends and insights, contributing to the development of the case study aimed at showcasing the product's capabilities to prospective customers. (See https://imeasureu.com/2020/07/15/returning-to-performance-with-inertial-sen sors/) • Contributed to the initial stages of a kicks feature within their product, primarily involved in data collection and labeling while gaining knowledge in Machine Learning and Signal Processing with Inertial Measurement Units. ## Education ### Bachelor of Engineering (Honours) in Biomedical Engineering University of Auckland Jan 2017 – Jan 2022 ### Professional Certificate in Data Engineering MIT xPRO Jan 2024 – Jan 2024 ### Ascham School Jan 2006 – Jan 2016 ## Contact & Social - LinkedIn: https://linkedin.com/in/isabella-murrell-678954150 --- Source: https://flows.cv/isabellamurrell JSON Resume: https://flows.cv/isabellamurrell/resume.json Last updated: 2026-04-01