Education: PhD in ECE from a top 5 US school
Years of Experience: 1.5 years
Prior Experience: ML Scientist in a Tier-2 company
Date of the Offer: June 2, 2020
Company: Amazon
Title/Level: Senior Applied Scientist/L6
Location: Seattle
Salary: 219000, Year 2: 13000 lumpsum
Stock bonus: 400000 First year
Benefits: Standard Amazon compensation
Other details: Negotiated the offer with a few hours to go before the deadline and the recruiter was nice enough to send me the updated offer soon.
Interview Details:
Phone Screen Round 1: One simple coding question on finding the kth largest element in an array. DL breadth covering when to use wide vs deep networks; whether GD/SGD always decreases loss; one LP question
Phone Screen Round 2: ML Depth, mostly focusing on my PhD research and publications. We discussed a particular paper of mine in great depth. It was more of an open ended discussion than the interviewer asking me questions.
Virtual Onsite (Bound by NDA):
Round 1: One hour research talk; got asked a couple of questions may be throughout
Round 2: Bar Raiser: LPs and ML Applied problem solving pertaining to Recommendation Systems and Error constrained classification
Round 3: ML Breadth, Mostly related to properties of loss functions; optimization in general; robustness of DL algorithms; 2 LP questions
Round 4: 30 mins of 3 LPs, A coding question on strings. Due to video conferencing issues, I couldn't go through the test cases. I could only finish writing the code which was largely correct after discussing my approach.
Round 5: Hiring Manager round. 25 mins of LPs followed by a deep dive of one of my previous internship projects.
Round 6: ML Depth, Was very much like my phone screen round 2. The interviewer particularly discussed one of my recent papers in great depth. Questions ranged from from clarification questions to what ifs?