Machine Learning Scientist

Machine Learning Engineer

Eli is looking for a Machine Learning Engineer to take ownership of and improve the model training and deployment pipeline using Data Version Control (DVC) and Google Cloud Platform (GCP) using 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.

Above all, you are committed to technical excellence and to delivering products with depth and rigor, 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, etc.).

• 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 commercializing 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 (<25 people), high-performing, and diverse team where everything you do results in tangible impact and shapes the company's trajectory.

• You'll be in an environment where people drive their own work, think creatively about open-ended problems, and solve them proactively.

• You'll get health insurance (medical, dental, vision, and more) to ensure you and your family stay physically and mentally at your best.

• You'll have flexibility over your schedule and vacations. We seek to hire great people, then give them the autonomy and space they need to achieve their goals.

• Although our office and R&D facilities are in Montreal, we have a distributed team. We prioritize asynchronous workflows and minimize meetings to focus on the work itself.

• You and your +1 will have unlimited free access to the Bota Bota spa in Montreal to recharge.

How to apply:

Sounds like you? Please apply using this link. Please note that we will not review applications made through other platforms. We look forward to learning about you!