Machine Learning Engineer

At Sunbit, we're building financial technology that puts people first. By combining intelligent decisioning with a personalized, transparent approach, we help consumers access the payment options that are right for them while empowering merchants to build stronger customer relationships.

Engineering is at the heart of how we make that happen. We build scalable distributed systems, cloud-native services, intelligent data platforms, and AI-powered solutions that enable real-time financial decisions at scale. By leveraging modern engineering practices and integrating AI into both our products and development workflows, we solve complex technical challenges, accelerate innovation, and continuously improve the way we build software. Every service, model, and architectural decision we make directly impacts our products, our customers, and the future of financial technology.

About the Role

Sunbit is looking for an experienced, independent team player who has great data & computer science skills, a passion for data, and excellent analytical and algorithmic skills. 

This role is diverse, encompassing AI/ML, data analysis, and backend engineering.

You will play a crucial role within a mission-critical team responsible for managing the heart and brain of Sunbit’s primary products. This involves working on core systems, such as POS underwriting models, models for merchant operations, Fraud investigator AI agents and more.

You will participate in cutting-edge risk management systems that safeguard the loan product's financial operation, as well as operational systems, with a direct impact on its financial success.

Our AI/ML team is part of the R&D group, so you will work closely with engineers and product managers as well.

Key Responsibilities

• Research and develop statistical behaviors, study domain-specific data.
• Develop state-of-the-art machine learning models end to end, including development, deployment, and continuous improvement. Both in-weight learning and in-context learning, for risk / fraud / operations related projects. This includes integrating models into production services and ensuring compliance with regulatory processes (e.g. providing evidence for production model audits).
• Operate backend infrastructure for training and deploying ML models, ensuring optimal performance and reliability.
• Develop and maintain Python code for translating ML model outputs into financial decisions.
• Analyze 15+ different data sources in order to train models and agents to catch fraudulent patterns. 
• Conduct analytical research on our model’s impact on the portfolio.
• Strategize and implement changes to enhance portfolio performance.

• M.Sc in quantitative discipline (preferably in Data Science, Computer Science, Mathematics, Statistics, or another related field with a strong emphasis on quantitative analysis).
• 3+ years of experience in developing and deploying ML models in a production environment.
• Knowledge of Data Science techniques, algorithms, and processes.
• Excellent analytical and algorithmic skills. Being able to conduct rigorous evaluation, infer conclusions and creatively offer solutions based on data analysis.
• Strong ownership and independence skills. Thrive in some tasks as the sole ML expert in a squad, comfortable taking ownership over the full life cycle of models and integrating versatile tasks (ML, data analysis, and Backend).
• Excellent teamwork skills.
• Effective communication skills to explain complex topics in English.

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