ML Research Engineer Usage, Subscription, and Payment Intelligence

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, Cond 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. Were 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 v .