Two candidates describe Autodesk machine learning interviews that stress resume deep-dives, ML theory, LLM deployment and data quality. Here is what each was asked, kept separate, and how to prepare.
$128K
Avg. Base Comp
$163K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Two candidates describe Autodesk interviews, and their loops look different. One had a phone interview for a Machine Learning Engineer role and was rejected. The other interviewed for an Applied Machine Learning Scientist role, went through a recruiter call, a hiring manager interview and a theory round, and received no offer. Because the second title differs, treat that account as an adjacent signal rather than a map of the ML Engineer loop.
The ML Engineer candidate's phone interview started with past experience and resume-based follow-ups on projects, then moved to model architecture, transformers, data and deployment. The candidate was also asked to design a GenAI product for the team, and was questioned in depth about GaN, most likely generative adversarial networks, and recent literature. Follow-ups were fundamental: why a given loss, why ReLU helps with vanishing gradients, how to clean data, how bad records were handled, and which evaluation metrics to use.
The Applied ML Scientist candidate's hiring manager conversation leaned on the team's project deploying LLMs. Their theory round covered why MSE is a poor loss for logistic regression, adjusted versus unadjusted R², and odds ratios framed around linear regression, along with the reasoning behind ML papers and NLP and LLM topics.
Across both accounts, the common thread is explaining why a method works. Know the assumptions and failure modes behind every project on your resume, and practice talking concretely about data quality, metrics and deploying language models.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Autodesk process.
Phone interview. Got asked about past experience. Followup on the projects based on resume. Questions about model architecture, transformers, data and deployment. Got asked about a hypothetical design GenAI product catered to the team.
Questions asked: They asked me in depth about GaN and the recent literature. More fundamental follow ups like why this loss, why relu helpls with vanishing gradients. How to clean data? How did you handle bad apples in data? Eval metrics
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Autodesk
Write a function to return the value of the nearest node that is a parent to both nodes.
| Question | |
|---|---|
| Rectangle Overlap | |
| Hurdles In Data Projects | |
| Priority Queue Using Linked List | |
| Spam Classifier | |
| Real-Time Hashtag Partitioning | |
| Cross-Region Inventory Sync | |
| Addressing Data Quality Issues | |
| Deciding Between Solutions | |
| Secure Messaging Platform | |
| Scalable Data Pipelines | |
| Client Solution Pushback | |
| Data Cleaning Experiences | |
| ReLu vs Tanh | |
| Merge Sorted Lists | |
| Subscription Overlap | |
| String Shift | |
| First to Six | |
| P-value to a Layman | |
| The Brackets Problem | |
| Find the Missing Number | |
| Scrambled Tickets | |
| Bagging vs Boosting | |
| Job Recommendation | |
| Find Bigrams | |
| Minimum Change | |
| Compute Deviation | |
| Raining in Seattle | |
| Permutation Palindrome | |
| Level Of Rain Water In 2D Terrain |
Synthesized from candidate reports. Individual experiences may vary.
The ML Engineer candidate describes a phone interview that opened with past experience and follow-ups on resume projects. Questions then covered model architecture, transformers, data and deployment. Be ready to defend the design choices on every project you list.
The same candidate was asked to design a GenAI product for the team, then questioned in depth on GaN (likely GANs) and recent literature. Follow-ups covered why a given loss, why ReLU helps with vanishing gradients, how to clean data, handling bad records and evaluation metrics.
A separate candidate, interviewing for an Applied Machine Learning Scientist role, reports the process opened with a recruiter call of roughly half an hour, mostly about background and experience. Expect to summarize your projects and ML work clearly.
This candidate describes an hour-long conversation mixing behavioral questions, technical ML system design and data science discussion. Much of it centered on the team's current project deploying LLMs, so concrete examples of serving and evaluating language models help.
The same candidate's third round focused on regression and related fundamentals: why MSE is a poor loss for logistic regression, adjusted versus unadjusted R², and how odds ratios are calculated, framed around linear regression. Interviewers also asked about the reasoning behind ML papers, plus NLP and LLM topics.
