Phenom Product Development Engineer II Machine Learning interview questions

The interview process was extremely long, inconsistent, and poorly managed. I went through five rounds over nearly a month: Round 1: Technical interview (cleared) Round 2: Technical interview focused on ML, GenAI, system design, and one DSA question (cleared) Round 3: Deep technical interview covering agents, MLOps, deep learning, and architecture (cleared) Round 4: Face-to-face technical interview (cleared) Round 5: HR discussion During the HR round, I was told that all my interview feedback was positive and that the team would discuss internally. A few days later, HR scheduled another HR call, only to tell me that they were looking for someone with stronger DSA skills. The disappointing part is not the rejection. Companies have every right to select the candidate they believe is the best fit. The issue is that DSA was never communicated as a key hiring criterion. Even after multiple technical rounds and positive feedback, they continued the process instead of identifying this gap much earlier. If DSA was a mandatory requirement, it should have been evaluated in the early stages, ideally in Round 1 or Round 2. Instead, they invested almost a month of my notice period, only to reject me for something that could have been screened upfront. The interviewers themselves were knowledgeable, and the technical discussions were interesting. However, the hiring process lacked transparency, proper planning, and respect for the candidate's time. My advice to future candidates is to ask HR upfront: Is DSA a mandatory evaluation criterion? What is the complete interview process? What is the expected hiring timeline? Is the budget already approved for the position? A lengthy interview process is acceptable when expectations are clearly communicated. Unfortunately, that was not my experience here. I would not recommend this interview process due to the lack of transparency and the significant amount of time invested without clear hiring criteria.