Machine Learning Researcher

Job Description

Job Description

Machine Learning Researcher

Location: San Francisco, CA
Work Style: Onsite, 5 days per week
Employment Type: Full-time
Experience: 5–10 years (including PhD or academic research)
Focus: Machine Learning Research, Predictive Modeling, LLMs, Multimodal AI, Credit Risk

About Our Client

Our client is a fast-growing fintech company transforming how credit is underwritten across Latin America.

The company's revenue has nearly tripled year over year, and its platform currently supports a loan portfolio exceeding $30 million.

They're looking to move beyond traditional credit underwriting models and develop entirely new machine learning architectures that can uncover predictive signals in complex, real-world financial data.

This is an opportunity to conduct meaningful ML research and deploy novel models into production, directly influencing real lending decisions at scale.

About the Role

Our client is hiring a Machine Learning Researcher to rethink how credit risk is modeled and evaluated.

This is a research-heavy role, with approximately 70% of the work focused on developing and experimenting with new ML architectures and 30% focused on productionizing successful models.

The ideal candidate has deep expertise in classical machine learning, predictive modeling, and applied research, with the curiosity to explore LLMs and multimodal approaches to underwriting.

You don't need a background in fintech or lending. The team is particularly interested in researchers who have modeled complex, noisy systems in fields like robotics, physics, chemistry, or other quantitative disciplines.

You'll work closely with the CTO and Head of Buy Now, Pay Later (BNPL) alongside a highly technical team of approximately 20 people in San Francisco.

What You'll Do

• Research and develop novel machine learning architectures for credit risk prediction.

• Explore LLM and multimodal modeling approaches beyond traditional XGBoost and gradient-boosted models.

• Analyze large, noisy proprietary datasets to uncover new predictive signals and features.

• Design and run experiments to evaluate model accuracy, robustness, and predictive performance.

• Apply rigorous evaluation techniques, including calibration, temporal validation, and data leakage detection.

• Build and improve predictive models for complex real-world financial systems.

• Deploy successful research models into production.

• Partner with engineering teams on model infrastructure, monitoring, and data provider integrations.

• Collaborate directly with technical and business leadership to define the future of AI-powered underwriting.

What We're Looking For

• 5–10 years of relevant experience in machine learning research, including qualifying academic or PhD research.

• Master's or PhD in Computer Science, Mathematics, Physics, Engineering, or another quantitative discipline from a top-30 university.

• Deep hands-on experience with classical machine learning, statistical modeling, and predictive algorithms.

• Strong Python programming skills for research, data science, and production machine learning.

• Experience modeling complex systems with noisy data, confounding variables, and uncertain outcomes.

• Strong understanding of model evaluation, calibration, temporal validation, and leakage detection.

• Ability to independently design experiments, test hypotheses, and develop new model architectures.

• Experience translating research into production-ready ML systems.

• Willingness to work onsite in San Francisco five days per week.

Important: This is not an LLM-only research position. Candidates must have strong foundations in classical ML and predictive modeling.

Bonus Experience

• Experience with LLMs, multimodal architectures, or representation learning.

• Research experience in robotics, physics, chemistry, or other complex scientific domains.

• Experience working with large proprietary datasets.

• Experience developing novel modeling approaches beyond established algorithms.

• Experience deploying research models into production environments.

• Exposure to credit risk, underwriting, financial services, or fintech.

Why This Opportunity

• Own meaningful ML research at a rapidly growing fintech company.

• Develop new architectures rather than simply optimizing existing models.

• Work with proprietary datasets and real-world financial outcomes.

• Spend the majority of your time on research while maintaining ownership of production impact.

• Collaborate directly with the CTO and a highly technical engineering team.

• Help reshape credit underwriting across Latin America.

• See research influence decisions across a $30M+ loan portfolio.

Ideal Candidate Profile

The ideal candidate is a research-first Machine Learning Researcher with strong mathematical foundations, deep classical ML expertise, and experience modeling complex real-world systems.

They've worked extensively with predictive models, know how to design rigorous experiments, and can identify meaningful patterns in messy datasets.

They're excited by the opportunity to move beyond XGBoost and traditional tabular modeling to explore novel LLM and multimodal architectures, while maintaining the discipline required to deploy reliable ML systems into production.