ML Research Engineer Usage, Subscription, and Payment Intelligence

About Chargebee

Chargebee is building AI-native billing and monetization infrastructure for modern, high-growth businesses. We serve transformative, category-defining customers such as Lambda, Conde Nast, Gorgias, HeyGen, Zapier, and CodeRabbit.

With headquarters in North Bethesda, Maryland, our team members are based primarily in India, the U.S., and Europe.

About the team

Chargebee AI Labs is a Chennai-based team of AI engineers, model behaviour researchers, and deployed-AI architects.

This is a research-centered team. We never assume that a foundation model, a fine-tune, or a simpler task-specific model is automatically the right solution. The team does the work of understanding the data, defining precise problems, and building strong baselines.

Chargebee processes billions of payment events a year across the revenue lifecycle: credits, subscriptions, invoices, payment attempts, retries, recoveries, refunds, credit notes, disputes, and collections activity. We're researching how this data can be used to build models that learn reusable representations of consumption, subscription and payment behaviour.

About the Role

We are looking for a Machine Learning Research Engineer who can explore new modelling approaches using subscription, usage, and payment event data.

You will begin with practical prediction problems and investigate whether techniques such as self-supervised learning, sequence modelling, and multi-task learning can improve performance across multiple use cases.

You don't need prior experience building a foundation model from scratch.

You should, however, have experience developing and evaluating machine-learning or deep-learning models, and a strong interest in learning how reusable representations can be trained from large-scale behavioural data.

This role requires both research curiosity and deep engineering rigour. You should be able to establish simple baselines, identify weaknesses in an experimental setup, implement more advanced approaches, and determine whether increased model complexity is justified.

What You Will Work On

Data and Problem Formulation

• A major part of the work will be defining the right machine-learning problem before choosing a model.
• You will investigate:
• How subscription, usage, and payment events should be represented
• How sequences should be constructed
• Which entities should define a sequence
• How labels and outcomes should be created
• How to handle delayed and missing outcomes
• How to avoid temporal and target leakage
• How behaviour differs across merchants, industries, regions, gateways, and payment methods
• How usage-based and consumption-driven pricing models behave differently from flat-fee subscriptions
• How models can generalise across different types of Chargebee customers
• How privacy, data access, and tenant separation affect model design

Prediction and Optimisation Models

You will initially establish strong rules-based and task-specific ML baselines for problems such as:

• Predicting whether a payment attempt will succeed
• Selecting an appropriate retry time
• Predicting whether a failed invoice is recoverable
• Prioritizing collection activity
• Identifying dispute risk
• Predicting involuntary churn
• Detecting unusual payment or subscription behaviour

You will compare advanced approaches against these baselines rather than assuming that a larger model will always perform better.

Representation and Sequence Learning

Where justified by the data and business value, you will explore:

• Self-supervised learning
• Masked-event prediction
• Next-event prediction
• Contrastive learning
• Multi-task learning
• Temporal and sequence models
• Transformer-based models
• State-space models
• Customer, subscription, invoice, and transaction embeddings
• Transfer learning across downstream tasks
• Hybrid tabular and sequential models

The objective is to determine whether a shared representation can improve multiple prediction and optimisation tasks.

Evaluation and Production Validation

You will help define realistic evaluation methods for financial and transactional models.

This may include:

• Time-based training and test splits
• Cross-merchant generalisation
• Calibration and uncertainty
• Class imbalance and rare-event evaluation
• Offline versus online model performance
• Comparison against existing rules and business processes
• Model drift and changing payment behaviour
• Business metrics such as payment success, recovery rate, avoided churn, and operational effort

What We Are Looking For (Required Skills)

• Approximately 3+ years of experience in machine learning, data science, applied research, or ML engineering.
• Experience training and evaluating machine-learning or deep-learning models.
• Strong understanding of statistics, probability, model evaluation, and experimental design.
• Strong Python programming skills.
• Practical experience with PyTorch, TensorFlow, JAX, scikit-learn, or equivalent frameworks.
• Experience working with structured, temporal, behavioural, transactional, or event-stream data.
• Ability to build data-processing and model-training pipelines.
• Ability to establish simple baselines before introducing complex approaches.
• Ability to read research papers and implement relevant ideas.
• Good software-engineering and debugging skills.
• Comfort working in an exploratory environment where the feasibility of an approach is not yet known.

Helpful Experience (Preferred Skills)

• Sequence modelling, time-series modelling, or recommender systems
• Self-supervised learning, transfer learning, or representation learning
• Payments, fraud, risk, credit, advertising, marketplaces, or customer-lifecycle modelling
• Survival analysis, uplift modelling, causal inference, bandits, or decision optimisation
• Model calibration and uncertainty estimation
• Large-scale data platforms and distributed training
• Model deployment, monitoring, and experimentation
• Meaningful open-source projects, publications, technical articles, or competition results

Education

A bachelor's or master's degree in computer science, machine learning, data science, statistics, mathematics, or a related discipline is preferred. A PhD is not required.

• Candidates with strong practical experience, research projects, open-source work, or evidence of independently learning and applying advanced ML techniques are encouraged to apply.

Benefits we offer

At Chargebee, we believe that feeling supported is the foundation for doing great work. We offer a comprehensive benefits package designed to support your personal, financial, and professional growth and ensure you thrive in every aspect of your life. Our commitment to supporting our employees is universal, while our benefits are thoughtfully designed to meet the unique needs of each region. We offer comprehensive packages that generally include:

• Health & Wellness: Robust coverage and access to programs to support your physical and mental well-being.
• Financial Security: Initiatives to help you build long-term financial stability.
• Work-Life Balance: Flexible options and resources to help you thrive both professionally and personally.

Chargebee is an equal opportunity employer. At Chargebee, we believe that diversity of backgrounds, perspectives, and experiences is not a good-to-have but a must-have as it strengthens us as a company and as individuals. We are committed to fostering an inclusive workplace where everyone feels valued, respected, and empowered to thrive.

We do not discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity/expression, age, national origin, ancestry, caste, citizenship, physical or mental disability, genetic information, marital status, military or veteran status, or any other characteristic protected by applicable law.