At R2, we believe that small and medium businesses are the productive engine of society. Small and medium businesses (SMBs) make up over 90% of companies in Latin America, yet they face a trillion-dollar credit gap. Our mission is to unlock SMBs’ potential by providing financial solutions tailored to their needs. We are reimagining the financial infrastructure of Latin America, where SMBs’ financial needs are met without ever having to go to a bank.

R2 enables platforms across Latin America to embed financial services that SMBs can leverage, starting with revenue-based financing. We are a high-performing, close-knit team with talent from organizations such as Google, Amazon, Nubank, Uber, Capital One, Mercado Libre, Globant, and J.P. Morgan.

We are entering a new phase of growth following a strategic investment from Ant International, focused on rapidly expanding our partner footprint, strengthening our credit and underwriting capabilities, and scaling operations across multiple markets. As part of this growth journey, eligible team members have the opportunity to participate in R2’s Phantom Share Program, a performance-based incentive designed to align our team with the company’s long-term success and value creation. We believe in building a culture of ownership, where those who help create value share meaningfully in it.

We are a data-first company. Machine Learning (ML) and Deep Learning (DL) are the core of our product, and data is the lifeblood for all of our decision-making. We are seeking a Lead Machine Learning Engineer (Lead MLE) to spearhead the design, development, and deployment of ML/DL models into production. As a Lead Machine Learning Engineer, you will own the end-to-end lifecycle of machine and deep learning systems at R2, from model deployment and monitoring to retraining, governance, and reliability in production. You will define the standards, tooling, and architectural patterns that allow data scientists and analysts to safely and efficiently ship models that directly power our credit and business decisions.

What you’ll work on:

Own ML systems & tooling in production :
- Define and evolve R2’s ML platform architecture, including model registries, feature pipelines, training infrastructure, and inference services.
- Evaluate and introduce tooling that improves developer velocity, reproducibility, and safety across the ML stack.
- Architect, implement, and deploy ML/DL models into production environments.
- Ensure models are optimized for scalability, latency, and reliability.
Automate Monitoring & Maintenance:
- Design and build automated monitoring systems to track model performance, drift, and data quality of ML/DL models that consume data from various sources.
- Establish alerting and retraining pipelines to maintain model performance and robustness sustainably over time .
Automate Data Science processes:
- Develop frameworks to automate recurrent Data Science workflows (e.g. model evaluation, and retraining).
- Standardize best practices across the team for reproducibility and efficiency.
Collaborate with technical teams & Lead other team members:
- Partner with Product, Engineering, and Risk teams to align ML/DL solutions to be productionized with business goals. Although you won’t be developing the models first hand initially, you will be involved with in-sample, out-of-sample, and production testing.
- Mentor junior and senior data scientists and analysts, fostering a culture of innovation, experimentation, and excellence.
Research & Innovate in the MLOps spectrum:
- Stay ahead of emerging ML & DL production techniques and technologies, evaluating their applicability to organizational challenges.
- Drive experimentation and prototyping of novel production and automation approaches.

Who you are:

You have at least five (5) years of experience with machine and deep learning engineering in a practical setting
You have a good understanding of fintech products, and risk management to interpret business data effectively
You have strong programming abilities (structured, object-oriented, and/or event-oriented programming) and are comfortable programming in Python/R and SQL (with a focus on Snowflake, preferably)
You have strong proficiency in ML/DL frameworks in Python (e.g. Tensorflow, PyTorch, Scikit-learn)
You are comfortable consuming data through APIs, SFTP, or straight-up CSVs
You are experienced with MLOps tools (e.g. MLflow, Kubeflow, Docker, Kubernetes, AWS microservices)
You have

Lead Machine Learning Engineer (LatAm Only) at R2