Job Description
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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 simplify financial services 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, scalable operating structure with low costs to serve and growing returns.
The organization has received recognition from prominent global publications and institutions, including Time’s 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks.
The organization is seeking a Staff Machine Learning Engineer, Recommendation Systems to help lead the technical direction of its recommendation systems. This is a hands-on senior individual contributor role for an experienced machine learning engineer who has successfully delivered ML systems at scale and can help shape the organization’s future engineering practices.
The successful candidate will serve as a technical anchor for the team, working on complex problems involving retrieval, ranking, multi-objective optimization pipelines, and the infrastructure required to serve millions of customers with low latency and high reliability.
Key Responsibilities
- Set the technical direction for recommendation systems, including architecture decisions that establish a foundation for future engineering development.
- Design and build production machine learning systems for retrieval, ranking, and multi-objective optimization that operate at scale while meeting demanding latency requirements.
- Make regular hands-on coding contributions as part of the engineering role.
- Lead technically demanding projects from initial design through production rollout.
- Partner with applied scientists to transition models from research environments into reliable, monitored production systems.
- Raise the team’s technical standards through design reviews, engineering mentorship, and improved practices around testing, experimentation, monitoring, and system design.
- Work directly with stakeholder teams to understand recommendation requirements and translate them into shared, reusable infrastructure rather than isolated solutions.
- Identify and resolve structural issues that affect team productivity, including tooling limitations, process challenges, and technical debt.
- Help establish scalable engineering practices for recommendation and personalization systems.
Required Qualifications
- Strong track record of building and operating large-scale machine learning systems in production, preferably recommendation, ranking, or personalization systems.
- Experience developing modern recommendation systems, including learned embeddings, semantic IDs, sequence models based on long-term user histories, and conversational recommendation systems.
- Deep understanding of the complete machine learning engineering lifecycle, including training, deployment, monitoring, data consistency, experimentation, and governance.
- Strong software engineering fundamentals.
- Fluency in Python, Scala, or equivalent programming languages.
- Practical experience with ML operations, including on-call responsibilities, incident response, and debugging systems under production load.
- Demonstrated technical leadership experience, either through a formal leadership position or by serving as a technical decision-maker for engineering teams.
- Ability to work effectively with ambiguity and translate broad business objectives into clear technical priorities.
- Strong written and verbal communication skills, with the ability to explain technical tradeoffs to both engineering teams and non-technical stakeholders.
Preferred Qualifications
- Experience with distributed systems.
- Experience with Apache Spark or similar large-scale data processing technologies.
- Experience designing and operating recommendation infrastructure serving large customer populations.
- Experience collaborating closely with applied scientists and cross-functional stakeholders.
- Experience improving ML engineering tooling, experimentation frameworks, monitoring systems, or technical processes.
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