Real, anonymous reports from people who interviewed for Machine Learning Engineer at Amazon, newest first and distilled into what to expect across the loop.
I first completed an online aptitude assessment, and after that I was invited to a virtual round. The questions in that virtual session were role-related and felt advanced, and there was also a smaller slice of SQL-focused questions.
My early steps were very assessment-heavy. After applying, I ran into an online assessment that covered English and typing/comprehension-style tasks. Then I moved into a virtual round where the focus was less on deep technical ML and more on how carefully I could follow instructions—attention to detail, data accuracy, and how I handled workflow scenarios.
Candidates typically start with an online assessment that includes aptitude tests and coding challenges, often focusing on algorithms and data structures. These assessments can be challenging and set the tone for the rest of the interview process.
The technical interviews often cover a mix of machine learning concepts, coding challenges, and system design questions, with a focus on practical problem-solving and foundational knowledge. Candidates should be prepared for both theoretical and applied technical questions.
Behavioral interviews are a significant part of the process, emphasizing alignment with Amazon's leadership principles and assessing candidates' communication skills and past experiences. Candidates should prepare to discuss their work history and how they handle various situations.
The interview process is often structured and can include multiple rounds across different days, with a mix of coding, technical, and behavioral assessments. Candidates should expect a quick turnaround in scheduling and decision-making.
Candidates frequently report feeling pressure during timed coding interviews and assessments, where performance can heavily influence progression. It's essential to practice coding under time constraints to build confidence.
Many candidates noted that the feedback loop is minimal, and decisions can feel abrupt based on performance in specific rounds. Preparing thoroughly and understanding the expectations can help mitigate the impact of unexpected outcomes.