About Chargebee

Chargebee is a leading provider of billing and monetization solutions, empowering businesses with recurring revenue models to streamline revenue and finance operations, capture actionable insights, and drive growth. Chargebee is trusted by businesses of all sizes, including Zapier, LegalZoom, Lambda, Freshworks, DeepL, Condé Nast, and Pret a Manger, and is proud to have been consistently recognized by customers as a Leader in Subscription Management on G2.

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

📌 ML Research Engineer — Usage, Subscription, and Payment Intelligence (Chennai)
🏢 Chargebee
📍 Chennai