About the role
Mission Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. Our mission: make data-driven, preventative care accessible to more people, before symptoms appear. In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives. Role Purpose As a Lead Machine Learning Engineer focused on MLOps within the Data Science Platform team, you will enable robust, reliable, and responsible machine learning workflows at scale. Working with high-volume data from proprietary sensors and devices, you will design and operate production-grade ML systems, ensuring strong experiment tracking, model lifecycle management, and scalable deployment across multiple healthcare domains. What You’ll Deliver in the First 6–12 Months • Build and productionize reusable MLOps components supporting scalable and reliable ML workflows. • Establish strong ML lifecycle practices including experiment tracking, evaluation, and reproducibility. • Enable robust and monitored ML systems aligned with healthcare-grade reliability and compliance
requirements. • Deliver reliable production inference workflows powering real-world outcomes for Neko members. • Partner across data, platform, and clinical teams to support scalable ML adoption across multiple use cases.
Responsibilities • Build reusable and scalable components supporting Machine Learning operations and platformization. • Own and maintain Machine Learning systems and platform services. • Establish and promote best practices across experiment tracking, model lifecycle, and evaluation. • Design and maintain production inference workflows delivering reliable and timely outputs. • Collaborate cross-functionally with Clinical Researchers, Data Scientists, ML Engineers, and Data Engineers. • Ensure ML systems and workflows align with healthcare and data privacy
