Machine Learning Engineering Manager

Jobgether

1d•Remote

About The Position

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Engineering Manager in Illinois. In this role, you will lead and scale a machine learning engineering team responsible for building intelligent systems that protect users and the platform from risk and fraud. You will combine hands-on technical leadership with people management, shaping both the teamâs growth and the technical roadmap. Working in a fast-paced, mission-driven environment, you will design and deploy production-grade ML models that directly impact millions of users. This position offers strong ownership, visibility, and influence across the organization. You will collaborate closely with operations and cross-functional partners to respond quickly to evolving challenges. The role supports a flexible, remote-friendly setup while delivering meaningful real-world impact.

Requirements

5 or more years of experience in data science or machine learning engineering
Strong proficiency in Python and SQL, with proven experience building and deploying ML models
Solid background in data analysis, statistics, and experimental design
Demonstrated leadership experience, including both technical guidance and people management
Ability to communicate complex concepts clearly to both technical and non-technical stakeholders
Experience working with production machine learning systems

Nice To Haves

Exposure to financial systems, lending, or risk and fraud use cases is a plus but not required

Responsibilities

Build, lead, and mentor a machine learning engineering team, taking a hands-on approach while assuming increasing management responsibilities as the team grows
Design, develop, and deploy machine learning models to strengthen risk management and fraud detection capabilities
Own technical direction within the risk and fraud domain, helping define strategy, architecture, and best practices
Write production-quality code and ensure models are reliable, scalable, and maintainable
Use SQL extensively to extract, transform, and prepare data for modeling and analysis
Design experiments using sound statistical methods to evaluate performance and guide decision-making
Partner closely with operations and other teams to respond rapidly to emerging risks and events