Manager, Machine Learning Engineeringnew

 

Tala is AI-native credit infrastructure for the global majority, combining proprietary risk intelligence with an expanding network of capital and distribution partners to power credit access at scale. Backed by more than $500 million in funding, Tala has distributed more than $7 billion in capital to more than 13 million customers across Africa, Latin America, and Asia—building one of the most robust datasets on thin-file borrowers anywhere in the world. Our mission is simple yet bold: to unleash the economic power of the global majority. We are looking for daring, data-driven leaders passionate about building the trust and credit infrastructure for the global majority.

 

Our pioneering work and proven impact have earned us consistent recognition, including being named to:

CNBC’s Disruptor 50 for five years.

CNBC’s World's Top Fintech Companies for two consecutive years.

Forbes’ Fintech 50 list for nine consecutive years.

Visionary investors, persuaded by the economic power of the global majority, have committed half a billion dollars in equity and debt to Tala's success.

 

Given the global nature of our team, we operate on a remote-first approach with office hubs in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India.

 

Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you!

The Role

We’re looking for a Manager, Machine Learning Engineering to lead Tala’s ML Platform team. This person will manage a team of Machine Learning Engineers responsible for building the platforms, frameworks, and infrastructure that enable our Data Science teams to securely train, deploy, monitor, and operate machine learning models at scale.

This is a player-coach management role. You’ll be responsible for developing and growing the team while also providing enough technical leadership to guide architecture, engineering practices, reliability, and production systems. The role has a particular focus on real-time machine learning inference and streaming data systems, as well as the platforms that support batch model development and deployment.