Results and Selection Rate

Out of the 450 students who met the eligibility criteria, only 19 were selected to move forward in the process. This highlights the highly competitive nature of the Turing hiring process.

Overall Experience:

The Turing interview process was challenging but rewarding. It provided a great opportunity to showcase my technical skills and learn more about generative AI.

Tips for Success:

• Practice problem-solving and Python programming: These are crucial skills for acing the coding challenge and technical assessment rounds.
• Prepare for common HR interview questions: Have clear answers prepared about your career goals and why you’re interested in Turing.
• Stay calm and focused during the proctored exam: Don’t let the pressure get to you.

By following these tips and putting in the effort, you can increase your chances of success in the Turing interview process. I hope this information is helpful! Good luck with your future endeavors!

Turing comes to our college for two roles Software Engineer and Data Analyst. The hiring process for both roles was rigorous and consisted of several elimination rounds. Here’s a breakdown of my experience:

Pre-Placement Talk:

Turing representatives held a pre-placement talk to explain their company culture, the roles they were hiring for, and the overall assessment process. This was a great opportunity to learn more about Turing and gauge if it would be a good fit for you.

Eligibility Criteria:

Turing had a set of eligibility criteria that students needed to meet to participate in the interview process. These included:

• B.Tech Percentage: 70% and above
• XIIth Percentage: 60% and above
• Xth Percentage: 60% and above
• No Current Backlogs (Previous backlogs may be considered)

Coding Challenge and Technical Assessment Round:

This round took place on the same day. It was a proctored exam, meaning your screen was monitored to ensure academic integrity. The coding challenge consisted of two questions:

• Question 1: Easy difficulty level (a good warm-up)
• Question 2: Medium difficulty level (tested your problem-solving skills)

The platform provided instant feedback, displaying the percentage of your code that functioned correctly. You needed to achieve at least 80% accuracy to progress.

Following the coding challenge, there was a technical assessment section. This involved evaluating the responses of a generative AI model to specific queries. You had to compare the accuracy and relevance of these responses and answer additional questions related to the model’s output.