Machine Learning Engineer

Branch is a leading AI-powered lending fintech with 50M+ downloads across India and Africa. We use alternative data and machine learning to expand financial access for millions of people traditionally excluded from the formal financial system. Founded by the former CEO of Kiva.org and backed by leading investors including Andreessen Horowitz, Visa, and the IFC, Branch combines mission-driven impact with world-class technology and scale.

In India, Branch operates as a regulated digital lending institution and Middle Layer NBFC, building trusted and accessible financial products for millions of customers across the country. Our 250+ member India team is growing rapidly and works across technology, data science, risk, product, and operations to solve high-impact problems at scale. 

Certified as a Great Place to Work in 2025, Branch offers the opportunity to build meaningful careers while shaping the future of inclusive fintech in one of the world’s fastest-growing digital economies.

About Role:
Branch’s ML based underwriting models are core to our business and directly drive business value. Every credit decision, fraud signal, and credit scoring model we deploy has a direct impact on real customers’ access to capital. We are looking for engineers who want to grow with us, take deep ownership, and have a genuine impact on how ML is built and deployed in production.

We are hiring ML engineers to join our ML teams in India. These teams own our core credit models, infrastructure, and the tooling that powers them. They also actively research and develop what comes next for ML at Branch for real credit and lending problems.

Here are some things you’ll do

• Build, maintain, and scale ML infrastructure end-to-end: feature computation pipelines serving real-time predictions at millions of requests per day, and training and deployment pipelines spanning classical and cutting-edge models.
• Build observability and drift monitoring to detect and respond to model degradation in production.
• Evaluate and address selection bias in credit models using techniques from reject inference to deep generative approaches.
• Explore using LLMs/agentic AI to generate features from raw data, automate segment analysis, inform credit and policy exploration, and more.
• Collaborate with credit underwriting, product, and backend teams to build signals from structured and unstructured data that identify creditworthy borrowers and power our underwriting models.

• 2–5 years of hands-on ML engineering experience in production environments, not just research or notebooks.
• Strong skills in building machine learning models using both structured and unstructured data.
• Strong Python proficiency, including ML libraries (XGBoost, scikit-learn, pandas, numpy) and software engineering fundamentals.
• Experience building or maintaining ML pipelines, training, evaluation, feature engineering, or model deployment.
• Have a diverse range of data skills, including experimentation and statistics, and have used these skills to inform business decisions.
• Experience with SQL and structured data, able to write non-trivial queries for feature extraction and analysis.

• GenAI/LLM experience
  • Prompt engineering, fine-tuning, or building LLM-powered workflows in production.
  • Familiarity with agentic system design and using structured LLM outputs.
• Credit risk or lending domain experience
  • Credit risk modeling or loan underwriting in any market.
  • Leveraging alternative data sources as predictive signals in emerging markets.
• Experience with Rust or compiled languages used in performance-critical services.
• Familiarity with RBI Digital Lending Guidelines or similar regulatory frameworks in India.
• Experience working in a fintech, neobank, or lending company.