I applied through a recruiter. The process took 6 weeks.

The process included:

• An initial recruiter screen
• A technical interview with a senior ML engineer
• 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.

Implement a tensor-like data structure from scratch in pure Python without using external numerical libraries, focusing on recursive handling of multi-dimensional shapes.