Lead AI Research Engineer

Felix Technologies, Inc.•California, CA

About The Position

At Félix, we're building the financial ecosystem for Latin immigrants in the U.S., starting with a revolution in remittances. Our core product is an AI-powered chatbot built on WhatsApp, allowing our users to send money home as easily as sending a text message. We leverage cutting-edge technology like AI, blockchain, and stablecoins to make cross-border payments faster, more affordable, and more accessible than ever before. We are a hyper-growth Series B company, backed by over $100 million in funding from top-tier global investors. Félix was selected as an “Endeavour Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award. We are a group of extremely talented and dedicated high-performers, united by our shared obsession with a single goal: empowering our customers. We are all owners of Félix, driven by a bias for action and a true experimentation spirit to get shit done with urgency and focus. 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. We are a team that is fiercely loyal to each other, where radical transparency and constructive feedback are how we grow and push for excellence. We are bold, we care less about what others are doing, and more about creating sustainable value and a product that truly makes our users' lives better. We are building the future, today.

Requirements

Ph.D. in Computer Science, AI, Mathematics, or a related quantitative field from a top-tier institution.
2 to 5 years of post-PhD experience in applied research or ML engineering, translating research into production.
Deep expertise in the mathematical foundations of LLMs, optimization, alignment techniques (DPO/RLHF), and fundamental model performance (accuracy, latency).
A true "bias for action." You thrive in high-velocity environments and care about measurable production gains over pure theory.
Proven ability to lead, mentor, and set the technical pace for a squad of elite engineers.
Equivalent competencies in any of the above will also be considered.

Responsibilities

Own the technical strategy to maximize the intelligence of our underlying models.
Advanced Fine-Tuning: Progress our models from parameter-efficient techniques to Direct Preference Optimization (DPO) and full-parameter fine-tuning.
Continuous Active Learning: Architect feedback loops that leverage low-confidence triggers, creating a virtuous cycle of training data.
Next-Gen Programmatic Intelligence: Advance our existing compiled prompt architecture to the next frontier, pushing the boundaries of programmatic optimization (DSPy 3.1 to 4.x+) for complex, multi-step agentic reasoning.
Model Compression: Spearhead compaction efforts to guarantee sub-second latencies for production models.
Publish breakthrough findings at top-tier conferences (NeurIPS, ICML, ICLR) and contribute to open source.