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.
Key Roles & Responsibilities
- Productionise ML workflows: build and maintain data pipelines for feature generation, ML model training, batch scoring, and real‑time inference using modern orchestration and container frameworks.
- Own model serving infrastructure: implement fast, reliable APIs / batch jobs; manage autoscaling, versioning, and rollback strategies
- Feature‑store development: design and operate feature stores and corresponding data pipelines to guarantee training–serving consistency.
- CI/CD & DevEx: automate testing, deployment, and monitoring of data and model artefacts; provide templated repos and documentation that let data scientists move from notebook to prod quickly.
- Observability & quality: instrument data‑drift, concept‑drift, and performance metrics; set up alerting dashboards to ensure model health.
- Collaboration & review: work closely with data scientists on model experimentation, production‑harden their code, review PRs, and evangelise MLOps best practices across the organisation.
Required Skills & Experience
- 3+ years as a ML / Data Engineer working on large-scale, data-intensive systems in cloud environments (AWS, GCP, or Azure), with proven experience partnering closely with ML teams to deploy models at scale.
- Proficient in Python plus one of Go / Java / Scala; strong software‑engineering fundamentals (testing, design patterns, code review).
- Hands on experience in Spark and familiarity with streaming frameworks (Kafka, Flink, Spark Structured Streaming)
- Hands-on experience with workflow orchestrators (Airflow, Dagster, Kubeflow Pipelines, etc.) and container platforms (Docker + Kubernetes/EKS/ECS).
- Practical knowledge of ML algorithms like XGBoost, LightGBM, transformers and deep learning frameworks like pytorch is preferred
- Experience with experiment‑tracking / ML model‑management tools (MLflow, SageMaker, Vertex AI, Weights & Biases) is a plus