The interview process was a process of 3 calls, first was a call with the recruiter, followed by a first round AWS focused technical interview and then a second round ML focused technical interview. The interviews were about 1 hour each, they were split into introductions, Q&A, and finally back and forth dialogue. The Q&A portions were technical and theoretical questions about different AWS/ML topics like variance vs bias, classification accuracy metrics, regularization, Sagemaker pipelines etc

The interview process was smooth and well-organized from start to finish. It struck a great balance between technical questions and behavioral ones, allowing me to demonstrate both my machine learning knowledge and how I approach problems. The questions were deep and practical, pushing me to think through core concepts like algorithms, model deployment, and optimization, which made the process feel more like a discussion than just a test of knowledge. I also appreciated the opportunity to learn more about the company—how teams collaborate, how problems are solved, and what the overall culture is like. It wasn’t just about evaluating my skills; it gave me a better sense of what working at the company would be like. Overall, it was a great experience that helped me learn and gave me a clear picture of the organization.