Tell me about your current role.

"Generally salesforce keep interviews in one two days hiring drive where each round eliminating.So i got the interview opportunity via referral so didnt gave OA but scheduled DSA round as very first round where 45 minutes and two medium to hard level questions should be done with almost optimised approach and time complexity explaination."

"Practical interviews, focussed on the role requirements. System design had unique twists, but nothing too out there."

"The round was easy - I had to go over my current responsibilities and my work. I was asked a coding question as well so prepare DSA well"

"Overall process is good, recruiter moves fast, interviewer is collaborative. The expectation for coding round is high - aim for optimal and bug-free solution."

"Behavioral and Technical rounds were very friendly and relaxed. The last round was very intense and a technical deep dive into software engineering fundamentals like design patterns, code quality evaluation, etc."

"What went well was that I was professional, and I’m pretty sure I communicated clearly. What didn’t go well was I wasn’t able to explain the impact."

"My interview process was thorough and gave me a good sense of both the technical and operational expectations of the role. What went well was the opportunity to discuss large-scale program delivery, stakeholder management, and how I have led complex cross-functional initiatives. In my current and previous roles, I've managed programs involving technology teams, business stakeholders, risk partners, and vendors, so I was comfortable discussing governance, prioritization, execution planning, and driving outcomes across multiple groups. I also enjoyed discussing how AI can be applied to solve business problems rather than focusing solely on the technology itself. One area that challenged me was some of the deeper AI and machine learning discussions. While I have successfully led AI-enabled initiatives and proof-of-concepts, my strength is program leadership and business value realization rather than building ML models myself. There were a few moments where I had to think carefully about technical trade-offs and model-related considerations. However, I approached those questions by focusing on how I would partner with data scientists and engineers, ask the right questions, manage risks, and ensure alignment with business objectives. Overall, I felt the process validated my strengths in delivery leadership, stakeholder management, strategic planning, and operational execution. It also highlighted areas where I can continue strengthening my AI technical depth. I left the process with an even greater appreciation for the importance of combining strong program management with a practical understanding of AI capabilities and limitations."

"It was easy and helped me understand the role better before asking questions. This helped me tailor my responses better"

"A sourcer had been nudging me on LinkedIn for a while, so I never did a normal recruiter screen. Once I sent my resume, two hiring managers in the same Siri AI/ML org wanted to talk, so Apple ran one combined process for both EPM roles instead of making me do two separate loops. I did two hiring manager screens, then a joint onsite with a data science manager, an ML engineering manager, another behavioral round with one of the hiring managers, and a final conversation with a very senior engineering leader. The whole thing felt extremely team-dependent and very niche, especially the ML metrics parts, which is what made it different from more generic big-tech TPM loops. I finished in early December and got rejected."

"A sourcer had been nudging me on LinkedIn for a while, so I never did a normal recruiter screen. Once I sent my resume, two hiring managers in the same Siri AI/ML org wanted to talk, so Apple ran one combined process for both EPM roles instead of making me do two separate loops. I did two hiring manager screens, then a joint onsite with a data science manager, an ML engineering manager, another behavioral round with one of the hiring managers, and a final conversation with a very senior engineering leader. The whole thing felt extremely team-dependent and very niche, especially the ML metrics parts, which is what made it different from more generic big-tech TPM loops. I finished in early December and got rejected."

"The process was relatively straightforward. I don't think there were any questions there were misleading and everyone was nice and was professional."

"The recruiter was helpful. The team itself is focused on supporting data management initiatives. But the questions were not all related to it. One of the questions was to code a palindrome- coding is not something I have done for decades. I have handled massive initiatives, reporting out to CIO and CFO of a large telecom, I understand technology , but to expect coding - IMHO a bit much."

"The interview process was fairly structured, starting with an initial screening followed by technical and/or managerial rounds. What went well was the clarity of questions and the opportunity to explain my thought process. One area that could have been better was the time allocated for certain discussions, which felt slightly rushed."