Search jobs

About us

Eli Health is making continuous hormone monitoring possible so users can 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 an on-site Applied Machine Learning Scientist to join our ML team and help solve challenging real-world problems involving imperfect, noisy, and complex datasets.

This role is primarily about scientific problem solving and applied machine learning, not ML infrastructure. You will work closely with the ML lead, who will support integration and productionization of your work

Where you'll spend the first half of your time

You’ll work on open-ended scientific and machine-learning problems, turning imperfect real-world data into robust, reproducible analyses and models. The results of these analyses will have to be communicated effectively to the broader team. The focus is on understanding the problem deeply, choosing the right methods, and validating that improvements are real and generalizable.

• Explore datasets and understand the underlying data-generating processes.
• Develop, evaluate, and improve machine learning, signal processing, and statistical models.
• Perform feature engineering, model selection, validation, and error analysis.
• Identify issues such as confounding, data leakage, measurement variability, and distribution shift.
• Design experiments and analyses to resolve uncertainty and guide modelling decisions.
• Investigate new modelling approaches, including classical ML and deep learning (when appropriate).
• Produce clear, reproducible Python code that isn’t limited to notebooks, and communicate findings to technical and non-technical stakeholders.

Where you'll spend the other half

You’ll stay closely connected to how models behave in the real world. You’ll investigate production performance, diagnose failures and unexpected behavior, and use those observations to drive new analyses, experiments, and model improvements.

• Develop analyses and models with the expectation that it will be deployed into production.