Back to back interviews, both one hour each with two different people. First was behavioral with some concepts, making sure you understand algorithms. Second interview was the technical coding portion.

The process included an initial recruiter screen, a technical interview with a senior ML engineer, and a final panel consisting of three separate one-hour technical interviews. The discussions focused heavily on ML systems, transformer architectures, inference optimization, quantization, deployment on constrained hardware, and debugging/model performance issues. The interviewers were technically strong and clearly cared about first-principles reasoning and systems-level thinking. The final round was very coding-heavy, with multiple live implementation and debugging exercises covering Python, PyTorch, and low-level tensor/data structure concepts. Overall, the technical bar was high and the conversations were interesting, especially for candidates interested in hardware-aware machine learning and infrastructure. However, the process leaned excessively toward live coding for an ML Engineering role, and the post-interview communication was disappointing. After completing the panel and sending multiple professional follow-ups, I never received any response or closure from recruiting.