Eli Health
Health Tech
MachineLearningEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Engineer at Eli Health. Skills: model training and deployment pipeline, Data Version Control (DVC), Google Cloud Platform (GCP), Python data science SDK, reproducible training, safe deployment, reliable production performance, machine learning on real-world biological data, model quality improvement, robust, traceable, and operationally trustworthy models. take ownership of and improve the model training and deployment pipeline with Data Version Control (DVC) and Google Cloud Pl”
What You'll Achieve.
models train reproducibly; models deploy safely; models perform reliably in production; models improve over time; improve model quality; detect problems early; build the foundation for faster, safer iteration
Industry & Context.
solve them proactively; distinguish signal from artifact
What They're Looking For.
Must Have
Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, or a related field, at least 5 years of experience building, deploying, and maintaining machine learning systems in production, comfortable taking true ownership and responsibility of technical systems and improving them over time, rather than stopping at a proof of concept, rigorous in your approach to reproducibility, traceability, and operational reliability in machine learning workflows, programming fundamentals, adept with the Python programming language, comfortable working in a cloud-based environment with version-controlled code and reproducible pipelines, experience training, evaluating, and deploying machine learning models on real-world data rather than academic or benchmark datasets, comfortable working with noisy, incomplete, and high-variability data, and know how to distinguish signal from artifact, can translate analytical findings into concrete improvements to models, features, validation methods, or deployment processes, communicate clearly and effectively, both verbally and in writing, and can explain technical trade-offs to multidisciplinary colleagues
Nice to Have
experience with DVC, GCP, and/or machine learning pipelines built around reproducible training and deployment workflows (e. g. , MLflow, Metaflow), experience with monitoring models in production, detecting regressions or drift, and supporting iterative model improvement after deployment, familiar with multimodal learning with images and tabular data, even if it has not been your primary focus
What You'll Do.
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
making models train reproducibly
perform reliably in production
and improve over time as new data and learnings emerge
work on real-world biological data
and changing conditions are the norm
actively contribute to ongoing analyses
including calibrations
and model performance investigations
translate findings into concrete improvements
improve the standards
and monitoring needed to ensure Eli’s models are robust
and operationally trustworthy
work closely with technical and domain experts to improve model quality
detect problems early
and build the foundation for faster
How You'll Work.
Team & Collaboration
work closely with technical and domain experts to improve model quality, detect problems early, and build the foundation for faster, safer iteration; explain technical trade-offs to multidisciplinary colleagues
Communication Scope
communicate clearly and effectively, both verbally and in writing; explain technical trade-offs to multidisciplinary colleagues
Full Job Description
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 t
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