You have opted out of job matches in .
To undo this, go to the 'Skills and Interests' section of your preferences.
Shared by 
17 days ago
Requirements and responsibilities
Role Overview: We're looking for an ML Engineer to join our Test & Learn Platform team. You'll build and scale experimentation and causal inference services from statistical engines to API integrations and cloud pipelines empowering business teams globally to make data-driven decisions.Responsibilities: Develop and maintain statistical/ML modules (DID, Synthetic Control, A/B Testing, Multi-Treatment Effects) in Python. Build and extend FastAPI services and integrate them with our web application via SDK wrappers. Design and optimize large-scale data pipelines using Py-Spark, Delta Lake, and Azure Data Lake. Profile and resolve OOM issues in Py-Spark jobs - optimize memory allocation, partitioning, broadcast joins, caching strategies, and Spark configurations. Deploy and manage workloads on Databricks, including job clusters, notebooks, and Delta Lake tables. Containerize and deploy services using Docker, Kubernetes, and CI/CD pipelines. Ensure code quality and security via Sonar Cloud, Snyk, and PyTest. Collaborate with data scientists and product teams to translate research into production-ready modules.
Optionally, you can add more information later (benefits, pre-screening questions, etc.)