Machine Learning Engineer

Job Description: Machine Learning Engineer Algorithms & Applied ML

About Chargebee:

Chargebee is a subscription billing and revenue management platform powering some of the fastest-growing brands around the world today, including Calendly, Hopin, Pret-a-Manger, Freshworks, Okta, Study.com and others. Thousands of SaaS and subscription-first businesses process over billions of dollars in revenue every year through the Chargebee platform.

Headquartered in San Francisco, USA, our 500+ team members work remotely throughout the world, including India, the Netherlands, Paris, Spain, Australia, and the USA.

Chargebee has raised over $480 million in capital and is funded by Accel, Tiger Global, Insight Partners, Steadview Capital, and Sapphire Ventures. And we're on a mission to push the boundaries of subscription revenue operations. Not just ours, but every customer and prospective business on a recurring revenue model.

Our team builds high-quality and innovative software to enable our customers to grow their revenues powered by the state-of-the-art subscription management platform.

About the Role

We are looking for a Machine Learning Engineer with strong algorithmic depth and hands-on experience building and rapidly iterating machine learning models in production products. This role sits at the intersection of theory and practice, requiring a solid grounding in mathematics and statistics along with the ability to translate data into models that deliver real business impact.

You will work closely with product, engineering, and data teams to design, deploy, and improve machine learning solutions that operate at scale and are core to our payments platform.

Problem Statement

The Payments team is building a suite of machine learningdriven products that operate on high-volume, high-dimensional transactional data to directly influence revenue growth and cost efficiency for a large customer base. As a Machine Learning Engineer on this team, you will work on problems such as optimizing payment success rates, improving retry and routing strategies, and building predictive models that inform real-time and batch decisioning systems.

The work involves designing and iterating on statistically sound, production-grade models, where improvements are driven by careful feature engineering, algorithm selection, and rigorous evaluation. These models run at scale, and even incremental gains have a measurable impact on the company's top and bottom line.

What You'll Do

• Design, develop, and iterate on machine learning models used in live, customer-facing products
• Apply supervised and unsupervised learning techniques, including:

*Classification and regression

*Clustering

*Dimensionality reduction

• Perform deep exploratory data analysis (EDA) to identify patterns, anomalies, and modeling opportunities
• Translate product and business problems into well-defined machine learning problems
• Build and evaluate models using appropriate metrics, validation strategies, and statistical tests
• Collaborate with engineering teams to deploy, monitor, and maintain models in production
• Improve model performance through feature engineering, algorithm selection, hyperparameter tuning
• Ensure solutions are scalable, maintainable, and well-documented

What We're Looking For

• 3+ years of experience building and deploying machine learning models in real-world, production environments
• Strong algorithmic foundations and problem-solving skills
• Excellent understanding of mathematics, probability, and statistics, including:
• Linear algebra
• Probability theory
• Statistical inference and hypothesis testing
• Solid experience with:
• Supervised learning techniques
• Clustering algorithms
• Dimensionality reduction methods
• Strong proficiency in Python and common ML/data libraries such as:
• NumPy, pandas, scikit-learn
• Experience performing data analysis at scale on large, complex datasets
• Working knowledge of databases (SQL and/or NoSQL) and data modeling
• Ability to reason clearly about modeling assumptions, trade-offs, and limitations

Also Important to Have

• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
• Familiarity with model monitoring, performance degradation, and data drift
• Experience running experimentation frameworks or offline evaluations
• Exposure to production ML systems and end-to-end model lifecycle management
• Prior experience in fast-paced, product-focused environments
• Payments experience is a plus

Why You'll Like This Role

• High ownership over ML systems that are core to the payments platform
• Opportunity to work on problems requiring both theoretical rigor and practical execution
• Direct visibility into how your work impacts revenue, efficiency, and scale
• Collaborative environment with strong engineering and product partners

Benefits

Want to know what it means to work for a company that genuinely cares about you Check out just a few of the benefits we give our employees:

We are Globally Local

With a diverse team across four continents, and customers in over 60 countries, you get to work closely with a global perspective right from your own neighborhood.

We value Curiosity

We believe the next great idea might just be around the corner. Perhaps it's that random thought you had ten minutes ago. We believe in creating an ecosystem that fosters a desire to seek out hard questions, and then figure out answers to them.

Customer! Customer! Customer!

Everything we do is driven towards enabling our customers growth. This means no matter what you do, you will always be adding real value to a real business problem. It's a lot of responsibility, but also a lot of fun.

Ready to build the future of recurring revenue at scale

Join the Chargebee tribe.