Lead Machine Learning Engineer (LatAm Only)
WFA Digital Insight
The demand for skilled machine learning engineers has grown significantly in recent years, with a reported 45% increase in job postings in 2023. As companies continue to rely on data-driven decision making, the need for experts who can design, develop, and deploy ML/DL models into production has never been more pressing. R2 is at the forefront of this trend, leveraging machine learning to unlock the potential of small and medium businesses in Latin America. With a strong focus on innovation and experimentation, R2 is an exciting place to work for those looking to make a real impact in the fintech industry. Before applying, candidates should be aware that a strong understanding of fintech products, risk management, and machine learning engineering is essential for success in this role.
Job Description
About the Role
As a Lead Machine Learning Engineer at R2, you will play a critical role in the design, development, and deployment of ML/DL models into production. You will be responsible for defining 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. The role requires a strong understanding of fintech products, risk management, and machine learning engineering, as well as excellent communication and collaboration skills.The Lead Machine Learning Engineer will work closely with the technical teams to align ML/DL solutions with business goals, and will be involved in in-sample, out-of-sample, and production testing. You will also mentor junior and senior data scientists and analysts, fostering a culture of innovation, experimentation, and excellence.
R2 is 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.
What You Will Do
- 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
- 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 and maintenance of ML/DL models, including designing and building automated monitoring systems to track model performance, drift, and data quality
- Establish alerting and retraining pipelines to maintain model performance and robustness sustainably over time
- Develop frameworks to automate recurrent Data Science workflows, such as model evaluation and retraining
- Standardize best practices across the team for reproducibility and efficiency
- Collaborate with technical teams to align ML/DL solutions with business goals
- Partner with Product, Engineering, and Risk teams to productionize ML/DL solutions
- Stay ahead of emerging ML & DL production techniques and technologies, evaluating their applicability to organizational challenges
What We Are Looking For
- At least 5 years of experience with machine and deep learning engineering in a practical setting
- Good understanding of fintech products and risk management to interpret business data effectively
- Strong proficiency in machine learning and deep learning frameworks, such as TensorFlow or PyTorch
- Experience with cloud-based technologies, such as AWS or Google Cloud
- Strong programming skills in languages such as Python or Java
- Excellent communication and collaboration skills
- Experience with agile development methodologies and version control systems, such as Git
- Strong understanding of data structures and algorithms
- Experience with data visualization tools, such as Tableau or Power BI
Nice to Have
- Experience with containerization using Docker
- Knowledge of Kubeflow or other ML/DL pipelines
- Experience with automated testing and deployment of ML/DL models
- Familiarity with DevOps practices and tools, such as Jenkins or CircleCI
- Experience with data science workflows and tools, such as Jupyter Notebooks or Apache Zeppelin
Benefits and Perks
- Competitive salary
- Opportunities for professional growth and development
- Collaborative and dynamic work environment
- Flexible working hours and remote work options
- Access to cutting-edge technologies and tools
- Participation 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
- Comprehensive benefits package, including health insurance and retirement plan
- Generous paid time off and holidays
- Access to training and development programs, including conferences and workshops
- Opportunity to work with a talented and experienced team
- Recognition and reward for outstanding performance and contributions
How to Stand Out
- Develop a strong portfolio of machine learning and deep learning projects to showcase your skills and experience
- Stay up-to-date with the latest advancements in machine learning and deep learning, including new technologies and techniques
- Practice explaining complex technical concepts in simple terms to improve your communication skills
- Be prepared to discuss your experience with agile development methodologies and version control systems
- Highlight your ability to work collaboratively with cross-functional teams, including product, engineering, and risk teams
- Research the company and the role to understand the specific requirements and challenges
- Prepare examples of your experience with automation, monitoring, and maintenance of ML/DL models
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