Machine Learning Engineer

An Ancient Greek word referring to “tools of the trade.”

ODAIA is a remote first organization, all our positions are WFH with frequent company and team based socials, events and meetings in-person and virtually.

ODAIA works with pharma commercial teams to grow their brands and engage with the right healthcare professionals (HCPs) at the right time. ODAIA's SaaS AI platform turns complex data into personalized insights, enabling commercial teams to understand their customers - (HCPs), predict prescribing behaviors and make informed decisions that bring therapies to patients faster.

ODAIAns (what we call ourselves) are inspired to reinvent the future of how non-technical people leverage data in their day-to-day lives. We are passionate about solving complex problems in data, AI, engineering, design, and product, so our customers don’t have to. We live by the notion that “simplicity is the ultimate sophistication;” and making simplicity scalable is an even bigger challenge.

That’s why we have a crazy talented team led by serial entrepreneurs, tech veterans, and life sciences experts.

To learn more visit  odaia.ai .

OUR MISSION
Reducing patients’ time to therapy by facilitating meaningful interactions with healthcare providers, through human‑centric software powered by AI.

We’re also on a mission to build an innovative, diverse, and ego‑free business, where trust, innovation and ownership are valued. You’re on a mission too? We’re here for it. We put an emphasis on career development for our employees, and the opportunities to grow are extensive.

WHAT’S ON OFFER
This is an excellent opportunity to join a rapidly growing innovator in a technically challenging and rewarding role. Reporting to the  Senior Engineering Development Manager , the  Machine Learning Engineer  will lead the development and enhancement of cutting‑edge machine learning models and advanced analytics that directly drive customer value. Leveraging deep expertise in ML algorithms and large‑scale data modeling, you will conduct research, experiment with novel techniques, and deploy innovative solutions that shape our predictive insights.

You’ll collaborate closely with data engineers and product teams across the organization to ensure our products leverage best‑in‑class AI capabilities, delivering robust, scalable, and impactful solutions.

WHAT YOU WILL DO

Develop, implement, and optimize advanced machine learning models, focusing on predictive modeling, time‑series analysis, causal inference, and hierarchical modeling

Design and build scalable ML pipelines using Python, SQL, Dagster, and Kubernetes to ensure efficient data workflows and robust deployment

Rigorously evaluate and iteratively enhance machine learning model performance using appropriate statistical methods and validation frameworks

Own end‑to‑end responsibility for the ML system lifecycle, from problem definition and exploratory analysis to deployment, monitoring, and ongoing iteration

Clearly articulate complex modeling concepts and analytical outcomes to both technical colleagues and non‑technical business stakeholders

Ensure solutions delivered are scalable, high‑quality, and promptly address evolving customer needs and business objectives

Continuously explore and integrate advances in ML research and statistical methodologies into ODAIA’s frameworks

Foster collaboration and knowledge sharing across teams, contributing to a culture of innovation and technical excellence

Please include a cover letter with your application outlining your relevant experience and interest in this role, you can describe a project you’ve worked on that relates to one of the “What you bring” or “What you will do” points, and share how you’d expand on it here at ODAIA

WHAT YOU BRING

Professional experience in developing, deploying and monitoring advanced machine learning models, particularly in analytics‑intensive or production environments

Strong foundation in software engineering best practices, including version control, clean and maintainable code, documentation, reproducible research, and collaborative development workflows

Practical experience with cloud‑based ML environments (AWS, Azure, or GCP) and…