u/Wrong_Hall_3079

Need it ASAP!!

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I recently appeared for the Amazon ML Challenge on Unstop.

I was assigned a problem set involving Binary Search Trees (BST) and Fenwick Trees (Binary Indexed Trees). I successfully solved both coding questions with optimal solutions, completed all MCQs and the SOP, and still had 18 minutes remaining on the clock.

Despite this, I was not shortlisted for the next round.

At the same time, many participants reported receiving significantly easier problem sets involving basic arrays and strings.

This raises a genuine question:

How was the evaluation normalized across candidates who received vastly different levels of difficulty?

Was there difficulty-based scaling?

Was time remaining used as a tie-breaker?

Were different question sets weighted differently?

How can candidates be confident that they were evaluated on a level playing field?

I completely understand that large-scale assessments with 75,000+ participants require automated evaluation systems. However, transparency in the evaluation criteria is equally important, especially when candidates are given different difficulty levels.

This post is not about a rejection.

It is about understanding whether the selection process adequately accounts for variations in question difficulty and whether candidates are being compared fairly.

I would appreciate any clarification from the organizers regarding the evaluation methodology.

\#AmazonMLChallenge #Unstop #CompetitiveProgramming #DataStructures #Algorithms #FairEvaluation #HiringChallenges

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I recently appeared for the Amazon ML Challenge on Unstop.

I was assigned a problem set involving Binary Search Trees (BST) and Fenwick Trees (Binary Indexed Trees). I successfully solved both coding questions with optimal solutions, completed all MCQs and the SOP, and still had 18 minutes remaining on the clock.

Despite this, I was not shortlisted for the next round.

At the same time, many participants reported receiving significantly easier problem sets involving basic arrays and strings.

This raises a genuine question:

How was the evaluation normalized across candidates who received vastly different levels of difficulty?

Was there difficulty-based scaling?

Was time remaining used as a tie-breaker?

Were different question sets weighted differently?

How can candidates be confident that they were evaluated on a level playing field?

I completely understand that large-scale assessments with 75,000+ participants require automated evaluation systems. However, transparency in the evaluation criteria is equally important, especially when candidates are given different difficulty levels.

This post is not about a rejection.

It is about understanding whether the selection process adequately accounts for variations in question difficulty and whether candidates are being compared fairly.

I would appreciate any clarification from the organizers regarding the evaluation methodology.

#AmazonMLChallenge #Unstop #CompetitiveProgramming #DataStructures #Algorithms #FairEvaluation #HiringChallenges

​

I recently appeared for the Amazon ML Challenge on Unstop.

I was assigned a problem set involving Binary Search Trees (BST) and Fenwick Trees (Binary Indexed Trees). I successfully solved both coding questions with optimal solutions, completed all MCQs and the SOP, and still had 18 minutes remaining on the clock.

Despite this, I was not shortlisted for the next round.

At the same time, many participants reported receiving significantly easier problem sets involving basic arrays and strings.

This raises a genuine question:

How was the evaluation normalized across candidates who received vastly different levels of difficulty?

Was there difficulty-based scaling?

Was time remaining used as a tie-breaker?

Were different question sets weighted differently?

How can candidates be confident that they were evaluated on a level playing field?

I completely understand that large-scale assessments with 75,000+ participants require automated evaluation systems. However, transparency in the evaluation criteria is equally important, especially when candidates are given different difficulty levels.

This post is not about a rejection.

It is about understanding whether the selection process adequately accounts for variations in question difficulty and whether candidates are being compared fairly.

I would appreciate any clarification from the organizers regarding the evaluation methodology.

#AmazonMLChallenge #Unstop #CompetitiveProgramming #DataStructures #Algorithms #FairEvaluation #HiringChallenges

I’m currently pursuing B.Tech CSE with a strong CGPA (9+), working on AI/full-stack projects, learning DSA seriously, and preparing for off-campus opportunities. My main issue is having less than 60% in 12th, which makes me ineligible for many campus drives.

Wanted to ask seniors and professionals:

Which NON-FAANG companies genuinely focus more on skills/projects than 12th marks?

Are startups and product-based companies more flexible with academics?

Has anyone with less than 60% in 12th cracked decent tech roles (8–20+ LPA)?

Which companies should I target for:

SDE

Full Stack

AI/ML

Cloud/AWS roles

Also:

Which platforms helped the most?

LeetCode

HackerRank

referrals

hackathons

LinkedIn networking

open source etc.

Would appreciate real experiences and company names from people who’ve actually seen this happen. Thanks!

I’m currently pursuing B.Tech CSE with a strong CGPA (9+), working on AI/full-stack projects, learning DSA seriously, and preparing for off-campus opportunities. My main issue is having less than 60% in 12th, which makes me ineligible for many campus drives.

Wanted to ask seniors and professionals:

- Which NON-FAANG companies genuinely focus more on skills/projects than 12th marks?

- Are startups and product-based companies more flexible with academics?

- Has anyone with less than 60% in 12th cracked decent tech roles (8–20+ LPA)?

- Which companies should I target for:

- SDE

- Full Stack

- AI/ML

- Cloud/AWS roles

Also:

- Which platforms helped the most?

- LeetCode

- HackerRank

- referrals

- hackathons

- LinkedIn networking

- open source etc.

Would appreciate real experiences and company names from people who’ve actually seen this happen. Thanks!