Interview Process: The process was intense and designed to filter for extremely high technical bar. It started with a recruiter screen, followed by multiple rounds of technical interviews focused on both theory and implementation. Initial Screen: A recruiter reached out and asked about my background, interest in financial markets, and familiarity with large-scale ML systems. Technical Phone Screen (1 hour): Heavy on algorithms and math. Included a LeetCode-hard level problem and questions on linear algebra and probability. Very little room for error — they expect near-perfect solutions under time pressure. Take-home / Modeling Challenge: Received a dataset with minimal guidance. Task was to design, train, and explain a model in a short turnaround (~3 days). Clear emphasis on signal detection, generalization, and overfitting prevention. Final Rounds (Virtual Onsite): ML Systems Design: Questions on scaling distributed training, low-latency inference, and pipeline reliability. Knowledge of JAX, PyTorch internals, and Nvidia/TPU hardware was expected. Research Deep Dive: Walked through one of my past papers. They drilled into every design decision, math derivation, and experimental choice. Panel of researchers — very sharp. Behavioral / Fit Interview: Surprisingly standard. Focused on how I handle ambiguous projects and work with PMs/engineers in high-stakes environments. Pros: Interviewers were brilliant and respectful. Challenging questions that made me a better researcher just by preparing. If you're at the cutting edge of ML/AI, it's one of the most intellectually rigorous interviews you can take. Cons: Extremely high bar with very little feedback post-interview. Heavy emphasis on real-time performance — even small mistakes were costly. The entire process felt more adversarial than collaborative at times. Advice to Candidates: Be rock solid on math (esp. probability, linear algebra), system-level design, and deep learning fundamentals. Brush up on financial applications of ML — even if it's not your background. Don’t expect leeway — precision and depth matter more here than at typical FAANG interviews.