Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.
Round 1: Just questions about my background Round 2: Two leetcode-style questions. Don't remember the first, second one was merging an array of linked lists into one sorted linked list. Round 3: A lot of machine learning conceptual questions, especially about neural networks, when I'd use them and how I'd troubleshoot in different situations. Round 4: Reading through a paper. Nothing was exceptionally difficult, but it was tough to prep with how unorganized everything was.
Resume specific questions. Tell me about your recent research in the area.
Python, ML, and SQL all key to interview. Business acumen tested as well.
Explain the diff between Data Science & ML
What if your data/model assumptions were wrong for this project?
Tell me about your work experience.
On your resume, I see this non-work project that is related to our business. Tell me more about it.
Given an array of characters representing mines on a minesweeper board, return an array of integers representing the number of mines around each cell.
Coding question in SQL.
Write a tool/function for reversing a string
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