About the job
Location Details: Bulgaria - remote
At GoDaddy, we believe in a flexible work environment that caters to the unique needs of each team. While some teams operate full-time in the office, others enjoy a hybrid setup, and many work entirely remotely. This position is fully remote, allowing you to work from the comfort of your home, with occasional visits to a GoDaddy office for team events or meetings.
Join Our Team
The Commerce Data & ML team plays a pivotal role in delivering intelligent and secure experiences within GoDaddy’s Commerce division. Collaborating closely with the Data Science team, we drive extensive machine learning and data engineering initiatives that empower GoDaddy Commerce to understand its customers, combat fraud, and tailor product experiences. Our partnership with the Risk team involves the development and maintenance of production ML Risk Models, including Transaction Risk Scoring and Merchant Risk Models. Additionally, we collaborate with the Growth team to facilitate data-driven marketing and customer segmentation through reliable pipelines and predictive models.
We have established a robust ML platform utilizing SageMaker Pipelines, Tecton feature store, Glue, GitHub Actions, and MLflow, supporting both batch and real-time inference. Recently, we have ventured into creating prompt engineering workflows for LLM-powered chatbots. As a Senior Machine Learning Engineer on the Commerce Data & ML team in Bulgaria, you will be instrumental in constructing production-grade pipelines and scalable ML systems that enhance fraud mitigation, customer insights, and AI-driven experiences.
What You'll Get to Do...
- Design, build, and maintain resilient ML pipelines for both batch and real-time inference using tools such as SageMaker Pipelines, EventBridge, MLflow, and feature stores.
- Collaborate with Data Scientists, Engineers, and stakeholders to implement, monitor, and optimize ML workflows, including model training, evaluation, drift detection, and deployment.
- Enhance and manage CI/CD pipelines for ML models, oversee model promotion and retraining workflows, ensuring seamless integration across various environments.
- Lead the end-to-end delivery of ML features from architecture and implementation to monitoring and iteration, while mentoring junior engineers and promoting best practices.
- Support operational excellence by participating in on-call rotations and responding to production incidents.

