Machine Learning Engineer Interview Experiences & Questions
66 real Machine Learning Engineer interviews from 33 companies: what each round covers, the exact questions, and who got the offer.
What to expect · Machine Learning Engineer
Based on 66 real interviews
Machine Learning Engineer interviews by company
The Machine Learning Engineer interview process
Most common round types: Coding (30), Behavioral (24), Technical (22), System Design (19), Machine Learning (15), Artificial Intelligence (11).
Real Machine Learning Engineer interview questions
- Burst Balloons
- Implement an efficient sparse matrix times vector multiplication.
- Describe your current role, team, project work, tools, AI usage, preferred language, and coding versus leadership split.
- How would you design a system like Google Street View?
- Here is how you were evaluated in the first coding rounds and what to expect in the onsite.
- How would you approach depth from a single image?
- Walk me through your experience and what you delivered in your previous projects.
- How would you think about aligning an AI model with the intended objectives and preventing misaligned or harmful behavior?
- Prep a 20 minute technical demo of LangSmith for a technical audience new to generative AI, and show prompt engineering, testing, and monitoring.
- Extract a clean dataset from this database using only SQL and Python.
- How do you work with people from different disciplines and onboard them onto the platform?
- Here's some prewritten TypeScript and React code with several bugs in it. Can you debug and fix it?
- Explain precision.
- Implement a k-nearest neighbor search.
- Tell me about your projects and previous experience.
- Design TikTok's video recommendation system.
- Here is a tooling scenario where the model has to make a plan and execute a tool. How would you solve it?
- Here is a transformer implementation with four annotated regions. Find the bug in each one.
- Can you walk me through your previous experience, including projects you've worked on and what you studied?
- In a provided Google Colab notebook, fill in the missing code to implement part of an LLM inference or output-processing step.
- Walk us through the agent you built and why you made the design choices you made.
- Explain temperature in LLMs.
- What's the general idea for the third question?
- Can you walk me through what you're doing now, what you've done before, and why you're interested in Waymo?
- How would you build a system to fetch videos similar to a given scene, like a rainy driving accident, from a very large dataset?
- Given a simple PDF-based workflow with long context windows, how would you improve reliability and performance?
- What happens if your model is overfitting?
- Deepdive on a project you worked on in the past
- Build a customer service AI agent for a fake outdoors company, pick 2 of 5 possible features to implement, and be ready to justify your choices.
- Give us a short 5 to 10 minute explanation of a technical topic of your choice.
- What's the difference between variance and bias for logistic regression vs. MLP?
- Simplify a path string and return the valid path.
- Pick an important project and walk me through why it mattered, how you approached it, how you built the team around it, what challenges you faced, and what impact it had.
- Tell me about an ethical or policy concern you raised under pressure.
- What distributed training modes are supported and not supported here?
- Here’s a dataset and a high-level prediction problem. How would you turn this into an ML problem and train a model?
- Can you walk me through your background and why it matches this computer vision role?
- Imagine you're on a team deploying a conversational AI model across sensitive topics, and internal testing shows it gives overly confident but factually wrong answers in high-risk contexts. How would you investigate…
- Hackrank Question with user ping and calculate counts within interval
- Given two sorted interval lists, merge them.
Showing 40 of 261 questions. Open any experience for every question and how the candidate answered.
Latest Machine Learning Engineer interview experiences
“Well structured. Before every round they gave us prep material and a mock round with a recruiter. Interviewers are knowledgeable and they are clear in what they want.”
“The coding round was the most tricky one, you don't know what problem you will get for the leetcode round. ML fundamentals, ML design is easy if you prepare for regular ML system designs. AI coding,…”
“The overall process is okay. Although there were multiple interview rescheduling for the onsite. I feel I did not do well for the ML System Design round. The question was asking how to design a chat…”
“The questions are not difficult but do require some actual experiences. Not much preparation can help.”
“2 coding rounds, 1 googliness round, 1 AI/ML depth. THe AI/ML was mostly going over resume, recent projects and some ml system design”
“technicals were passable with some prep. for onsite, it helps to be familiar with more exotic forms of pipeline parallelism”
“The overall process is smooth. Did asked very deeply on one of your chosen projects. Asked how to curate the training data, model training strategy, model evaluation and inference, and deployment.…”
“The process was much quicker and smoother than at other companies. Recruiters shared some pointers on what to prepare, and mostly it was around that only. What did not go well was that one interview…”
“What caught me off guard was that one of the coding rounds was not generic LeetCode at all. It was AI coding, with things like sparse matrix times vector multiplication and k-nearest neighbor…”
“People are nice, questions are fair, but some questions might be niche. Had a hard time understanding the questions.”
“Overall, the interview process was smooth and well-structured. The interviewers were friendly and gave me a good opportunity to explain my experience and approach to problem-solving. I felt the…”
“The interview was "Python Coding" so I prepared for Data Structures and Algorithms but I wasn't expecting this question.”
“Was quite smooth and easy to follow. Recruiter screen was not so technically rigorous but the case study was.”
“The process generally be good, and enjoyable, not too much difficult. but needs to explain deeply to interviewer.”
“The system design interview was data collection, SFT and RL stages. It went well for me. It was focused on LLM post training techniques. The ML coding was worst for me although I practiced the same…”
“The overall process was well organized, and the recruiter was clear about what to expect. The interviewers were professional and friendly. The technical round was more open-ended than I expected, so…”
“traditional interviews for MLE. coding rounds are AI-enabled meaning you can use claude or codex to write the code.”
“The process is kind of cold. All tasks are performed on codesignal. You are basically do the assignment by yourself with the computer and camera on.”
“The problem was challenging. There were many conditions you had to satisfy and some aspects were a bit underspecified. The interviewers were stone faced and gave zero commentary. I ended up making a…”
“Interviewer was very comforting but focus on more Eval part. Prepare eval better along with failover strategy, model choices.”
“What went well: The live coding and system architecture rounds went smoothly. I was able to articulate my design choices clearly, especially around API design, database strategy, and maintaining…”
“Went harsh and the interviewer was rude while asking the questions it was 45 mins but they extended it beyond 1 hr”
“It started with self-introduction and the interviewer wanted to know more about my research. So we main discussed about the papers and the potential topic I can do during the internship”
“It was a good interview overall, I was just not prepared for it. Because it was a researcher role, i was first ask an my experience doing AI/ML research then we moved on to the technical problem,…”
“The process was fairly quick. Recruiter gets back very fast. Good experience. All interviews completed within a week.”
“The overall process was rigorous and comprehensive, more challenging than many interviews I have done with other large technology companies. However, the recruiter’s guidance was accurate and…”
“The overall process was rigorous and comprehensive, more challenging than many interviews I have done with other large technology companies. However, the recruiter’s guidance was accurate and…”
“Seemed a little disorganized. Questions off the top of the interviewers' heads. No structure given to the interviewee ahead of time. Everyone was very nice”
“The process was well organized and technically rigorous. Interviewers focused on problem-solving and practical imaging knowledge. What caught me off guard was the depth of questions on camera…”
“Very disorganized. They forget you are interviewing with them unless you remind them. The technical round was very confusing, as the interviewer kept contradicting themselves and made a sarcastic…”
Frequently asked questions
How many rounds are in the Machine Learning Engineer interview?
Candidates report a typical 3 rounds, usually including a recruiter screen, online assessment, phone screen, take-home assignment.
How hard is the Machine Learning Engineer interview?
Candidates rate it 3.3/5 on average (medium), across 66 interviews.
How long does the Machine Learning Engineer hiring process take?
About 3 weeks from first contact to decision, based on reported timelines.
What percentage of Machine Learning Engineer candidates get an offer?
43% of candidates with a final result got an offer (23 of 53).
What does the Machine Learning Engineer interview focus on?
The most common round types are Coding, Behavioral, Technical, System Design.