Huawei ML engineer candidates report project deep-dives, LeetCode-style coding, and deep learning fundamentals such as CNNs, batch normalization, and transformers, followed by a manager or HR conversation.
$125K
Avg. Base Comp
$150K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Across three candidate reports for Huawei machine learning roles, the common thread is that interviewers want to hear what you actually built. Two candidates describe technical rounds that began with a detailed walkthrough of past research or projects, and one says the questioning continued until they hit the limit of what they could explain. Expect to defend design choices, challenges, and results rather than summarize them.
The theory questions candidates mention are fairly classic deep learning: CNNs and their use cases, activation functions, the number of learnable parameters in batch normalization, transformer embedding dimensions, and model compression. One candidate also names PyTorch, since it was listed on the resume. Another was asked about pointers and basic array work.
Coding appears in two of the three reports. One candidate describes a binary tree traversal alongside LeetCode-style problems. Another describes a LeetCode medium combining backtracking and dynamic programming inside a one-hour technical round split between projects and coding. Practice explaining your approach out loud under time pressure.
The later rounds differ. One candidate describes a casual final conversation with a manager or team lead. Another found the HR round disorganized, with hypothetical workplace questions unrelated to the role, so it helps to ask early what the role and team involve. Outcomes were mixed signals, a rejection, and a withdrawal after passing the technical screen. With only three reports, treat these details as individual experiences.
Synthesized from 3 candidate reports by our editorial team.
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Synthesized from candidate reports. Individual experiences may vary.
One candidate reports a recruiter phone screen followed by an email invitation to the technical rounds. Another says the recruiter allowed them to skip the online assessment and move forward. Early steps differ between candidates, so confirm the sequence with your recruiter.
One candidate describes a straightforward behavioral test with personality and general-fit questions, and says they were told a second attempt could be requested if needed. The other reports do not mention this step. Prepare brief, honest answers about your working style and motivation.
Candidates report technical rounds that open with a detailed walkthrough of research or projects, and one says follow-ups continued until they ran out of detail. Coding questions include a binary tree traversal and a LeetCode medium on backtracking and dynamic programming. One round lasted a full hour split between projects and coding.
Candidates report questions on CNNs and their use cases, activation functions, batch normalization parameter counts, transformer embedding dimensions, model compression, and PyTorch. One also mentions pointers and basic array questions. Be ready to explain any tool or framework you list on your resume.
One candidate describes a casual final conversation with a manager or team lead. Another reports an HR round with off-topic hypothetical questions and little discussion of the role. Prepare to ask about the role and team expectations, and to answer unexpected questions calmly.
