Maching Learning Engineer
About us Eli Health is making continuous hormone monitoring possible, enabling users to support their daily and long-term health. No more waiting days to track key biomarkers—get results you can use within minutes. Eli’s flagship product, Hormometer™, is the first instant hormone monitoring platform to deliver results from saliva to mobile app—anytime, anywhere. Developed over six years of R&D, over 2,000 product iterations, and backed by a dozen patent-pending innovations, Eli’s award-winning platform turns hormones into measurable signals you can track and improve. Just as the thermometer and glucometer have transformed health for millions, Eli’s platform is poised to be the next major evolution in tracking changes in stress, endurance, sleep, and more. About the role Eli is looking for a Machine Learning Engineer to take ownership of and improve the model training and deployment pipeline with Data Version Control (DVC) and Google Cloud Platform (GCP), leveraging our Python data science SDK. This is not a research-only role: you will be responsible for making models train reproducibly, deploy safely, perform reliably in production, and improve over time as new data and learnings emerge. You will work on real-world biological data, where noise, variability, and changing conditions are the norm. In addition to owning the pipeline, you will actively contribute to ongoing analyses, including calibrations, pilot studies, error analyses, and model performance investigations, and translate findings into concrete improvements. This role sits at the intersection of machine learning and software engineering. You will help improve the standards, tooling, and monitoring needed to ensure Eli’s models are robust, traceable, and operationally trustworthy. You will work closely with technical and domain experts to improve model quality, detect problems early, and build the foundation for faster, safer iteration. About you Above all, you are committed to technical excellence and to delivering products with depth and rigour, with the judgment and ownership to go above and beyond when it truly matters. You have a Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, or a related field. You have at least 5 years of experience building, deploying, and maintaining machine learning systems in production. You are comfortable taking true ownership and responsibility of technical systems and improving them over time, rather than stopping at a proof of concept. You are rigorous in your approach to reproducibility, traceability, and operational reliability in machine learning workflows. You have strong programming fundamentals, are adept with the Python programming language, and are comfortable working in a cloud-based environment with version-controlled code and reproducible pipelines. You have experience training, evaluating, and deploying machine learning models on real-world data rather than academic or benchmark datasets. You are comfortable working with noisy, incomplete, and high-variability data, and know how to distinguish signal from artifact. You can translate analytical findings into concrete improvements to models, features, validation methods, or deployment processes. You communicate clearly and effectively, both verbally and in writing, and can explain technical trade-offs to multidisciplinary colleagues. Nice-to-haves You have experience with DVC, GCP, and/or machine learning pipelines built around reproducible training and deployment workflows (e.g., MLflow, Metaflow). You have experience with monitoring models in production, detecting regressions or drift, and supporting iterative model improvement after deployment. You are familiar with multimodal learning with images and tabular data, even if it has not been your primary focus. Why you’ll love working at Eli You’ll work with a group of talented and mission-driven people eager to improve lifelong health at scale. You’ll be part of the core team developing and commercialising the first product that monitors hormonal data daily and over a lifetime. You’ll join the early-stage startup phase and have a wide-reaching impact in a constantly evolving, fast-paced environment. You’ll be part of a small (Apply To This Job