Staff Machine Learning Engineer

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Company Overview

A leading digital bank in Latin America serves more than 135 million customers across Brazil, Mexico, and Colombia. The organization is transforming the financial services industry by leveraging data and proprietary technology to develop innovative products and services.

Guided by a mission to reduce complexity and empower people, the organization supports customers throughout their financial journey while promoting financial access, responsible lending, and transparency. Its business model combines an efficient and scalable operating structure with a low cost to serve and growing returns.

The organization has received recognition through several industry awards, including Time’s 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks.

About the Role

The AI Core team is expanding the impact of artificial intelligence initiatives with the goal of making AI a primary driver of critical decision systems. The organization is seeking a Staff Machine Learning Engineer to lead high-impact research projects that connect state-of-the-art AI with production-grade financial systems.

The role will focus on solving complex and ambiguous problems using Deep Learning and Foundation Models while ensuring architectures remain scalable, efficient, and capable of generating measurable business outcomes.

Research Execution & Technical Leadership

• Lead and independently execute complex applied research initiatives.
• Build and optimize advanced architectures, including Transformers and Graph Neural Networks (GNNs).
• Develop solutions for critical applications such as Credit, Recommendation Systems (RecSys), Generative AI, and real-time inference.
• Address difficult and ambiguous machine learning problems requiring collaboration across Data, Infrastructure, and Product teams.
• Bridge the gap between research and production by designing architectures that account for MLOps requirements.
• Optimize models for latency, interpretability, scalability, and cost efficiency.

Strategic Impact & Collaboration

• Develop innovative solutions addressing complex project-level challenges.
• Apply the latest platform and AI research advancements to downstream production models.
• Collaborate with cross-functional teams to integrate research outputs into critical decision-making systems.
• Establish technical standards for experimentation, model evaluation, and code quality.
• Encourage peers and engineering teams to maintain high technical and performance standards.

Mentorship & Function Contribution

• Serve as a technical mentor to senior engineers and researchers.
• Provide guidance on deep learning fundamentals, problem formulation, and research methodologies.
• Contribute to organizational growth through hiring activities, including technical interview panels.
• Lead internal initiatives and task forces focused on improving the machine learning lifecycle.
• Contribute to thought leadership through research collaborations and internal technical publications aligned with strategic objectives.

Required Qualifications

• 5–7+ years of professional experience in applied AI and machine learning.
• Proven track record of delivering research-driven machine learning systems into production environments.
• Deep expertise in Deep Learning architectures, particularly Transformers, Multimodal models, or GNNs.
• Strong programming skills in Python.
• Proficiency with machine learning frameworks such as PyTorch, JAX, or TensorFlow.
• Strong understanding of MLOps and the practical constraints associated with deploying machine learning models at scale.
• Advanced problem-solving skills, particularly in formulating machine learning problems involving uncertain, incomplete, messy, or unavailable data.
• Strong communication skills with the ability to explain complex technical concepts to technical peers and cross-functional stakeholders.
• Experience conducting large-scale experimentation and A/B testing to validate research hypotheses.

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