Machine Learning Data Engineer; LLM & Generative AI

At Félix, we are building the indispensable financial companion for Latinos in the US. We combine an AI-powered, conversational-first interface with real-time financial infrastructure to make cross-border money movement as easy as sending a text. Starting with fast, affordable remittances powered by AI and crypto rails, we are expanding into credit, savings, and wallet services to support the complete immigrant financial journey.

Our ambition is to deliver a white-glove financial experience with the simplicity of a conversation to a community the traditional financial system has historically overlooked.

We are a hyper-growth Series C company, backed by over $300 million in funding from top-tier global investors, including Andreessen Horowitz, QED, Castle Island, Switch Ventures, HTwenty, Monashees, General Catalyst Customer Value Fund. This isn't just about the numbers; it's a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavor Entrepreneur” and was a recipient of the Cross Tech Fintech Startups Award.

Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long because we are obsessed with our customers. We get things done with urgency and focus, driven by extreme ownership over our impact.

We collaborate without ego, fostering radical transparency and fierce loyalty so we can grow together. Because we aim for insanely great rather than just good enough, we stay insatiably curious—always experimenting, building the future today, and delivering a product that truly makes our users' lives better.

We are seeking an innovative and highly technical Machine Learning Data Engineer to build the foundational data infrastructure that powers our Large Language Models (LLMs) and autonomous AI agents. In this role, you will sit at the bleeding edge of generative AI, focusing on the end-to-end data lifecycle—from orchestrating real-time telemetry to architecting the pipelines that train and refine our models.

You will be responsible for ensuring our models are fed with high-quality, diverse, and well-structured data to drive continuous performance improvements.

• Automated Fine-Tuning Pipelines:
  Design, build, and maintain highly scalable, automated data pipelines to extract, clean, and format massive datasets specifically for LLM fine-tuning (Supervised Fine-Tuning, RLHF, and DPO).
• Real-Time Agent & LLM Evaluation:
  Develop robust telemetry and sampling systems to ingest real-time production data. Use this data to continuously measure, evaluate, and benchmark the real-time performance, latency, and accuracy of live LLMs and AI agents.
• Synthetic Data Generation:
  Architect and scale synthetic data generation workflows. Leverage foundational models to create high-variance synthetic datasets to augment training data, cover edge cases, and mitigate bias where real-world data is scarce or privacy-restricted.
• Data Quality & Curation:
  Implement automated data quality checks, deduplication strategies, and toxicity filtering to ensure the highest standard of data integrity entering the model training pipelines.
• Infrastructure & MLOps Integration:
  Partner closely with ML Researchers and Dev Ops to integrate data pipelines with model training infrastructure, ensuring seamless continuous integration and continuous deployment (CI/CD) for model updates.
• Vector Database Management:
  Manage and optimize data flows into vector databases to support Retrieval-Augmented Generation (RAG) capabilities for our agentic workflows.

• Education:…