# Machine Learning Engineer Interview Experience ## Overview **Company:** Anthropic **Role:** Machine Learning Engineer **Level:** Entry Level / L3 **Submitted:** 5 months ago **Location:** United States **Interview Result:** Rejected **Interview Date:** A year ago **Timespan:** 3 weeks **Difficulty:** Difficult ## Interview process I applied through Anthropic's site and went through a recruiter screen, a technical assessment, another technical round, a panel, and then a final onsite-style step that for me was two more technical tasks. A lot of the process centered on SQL and Python data work, first pulling data, then cleaning and analyzing it, but the ethics questions were way heavier than what I usually see. The panel felt like a work simulation about deploying a conversational AI model safely in high-risk settings, with different interviewers pushing on misuse, alignment and credibility, and privacy. My final round was a Python data-transformation task plus a debugging exercise on a prewritten pipeline, and the debugging is where I ran out of time. I did not make it to the hiring manager conversation after that, so I was rejected at the final technical stage. - Recruiter screen - Online assessment - Technical interview - Other - Final round ## Interview tips I wish I had prepped more for an ML workflow interview, not just a coding interview. Practice pulling data with SQL and Python, cleaning messy datasets in pandas, and debugging medium-sized Python pipelines under time pressure. I'd also spend real time on compliance, alignment, ethics, privacy, and decision accountability, because they cared about that a lot more than most companies I talked to. And I would make sure to talk through my reasoning the whole time, especially when the prompt is ambiguous. ## Company culture I came away feeling like they care a lot more about standards than just pedigree. After the basic background questions, the process kept coming back to ethics and human impact, and whether I could make safe decisions around data and models. Even the panel felt like they wanted to simulate real deployment risk instead of just checking if I could code. ## Questions asked ### Recruiter screen The recruiter screen was pretty basic, but they spent a lot of time trying to understand how entry-level I really was. It felt like they were checking my background, flexibility, and whether I was still worth moving forward without prior ML work experience. #### Question types asked - [Behavioral](/content/questions?type=behavioral/index.html) - [Project Discussion](/content/questions?type=project-discussion/index.html) - [Machine Learning](/content/questions?type=machine-learning/index.html) #### Specific questions asked - Can you walk me through your education, courses, certifications, and any machine learning experience you have? - Do you have any prior work or volunteer experience? - If this ended up being an internship, would you still take it? ### Overview The first technical step was more about pulling data than doing trick questions. I had to work from a demo company dataset using only SQL and Python, and then they shifted into a lot of questions about whether the data was actually safe and appropriate to use. #### Question types asked - [SQL](/content/questions?type=sql/index.html) - [Technical](/content/questions?type=technical/index.html) - [Machine Learning](/content/questions?type=machine-learning/index.html) - [Artificial Intelligence](/content/questions?type=artificial-intelligence/index.html) #### Specific questions asked - [Extract a clean dataset from this database using only SQL and Python.](/content/questions/5787/index.html) - Is the dataset properly formatted? - Is it compliant enough to use for deeper analysis or visualization? - What ethical and safety trade-offs do you see in using this data? ### Overview The next technical round felt like the follow-on from the assessment. Instead of mainly pulling data, I had to clean what I had extracted and do more analysis in Python without leaning on outside NLP libraries, so it felt closer to actual ML workflow. #### Question types asked - [Coding](/content/questions?type=coding/index.html) - [Technical](/content/questions?type=technical/index.html) - [Data Analysis](/content/questions?type=data-analysis/index.html) - [Machine Learning](/content/questions?type=machine-learning/index.html) - [Artificial Intelligence](/content/questions?type=artificial-intelligence/index.html) #### Specific questions asked - Using only Python, clean the extracted file and do further analysis without third-party NLP packages like spaCy or Hugging Face. - How would you think about AI alignment here? - What about human impact and decision accountability? ### Overview The panel was the most distinctive part of the process for me. It felt more like a real-world simulation than a normal interview, and there were three people, each pushing on a different safety area. Before the scenario, they also had me walk back through the extraction and cleaning work from the earlier rounds. #### Question types asked - [Artificial Intelligence](/content/questions?type=artificial-intelligence/index.html) - [Machine Learning](/content/questions?type=machine-learning/index.html) - [Technical](/content/questions?type=technical/index.html) #### Specific questions asked - [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 and mitigate that problem?](/content/questions/5788/index.html) - How would you think about model misuse scenarios? - How do alignment and credibility factor into your approach? - What data ethics and privacy issues would you check? ### Overview The final step I reached was basically two technical rounds. The first was an ambiguous Python data-transformation task, and the second was a debugging exercise on a prewritten Python pipeline. I got through most of the coding, but I ran out of time on the debugging, and I did not make it to the hiring manager conversation after that. #### Question types asked - [Coding](/content/questions?type=coding/index.html) - [Debugging](/content/questions?type=debugging/index.html) - [Technical](/content/questions?type=technical/index.html) - [Machine Learning](/content/questions?type=machine-learning/index.html) #### Specific questions asked - Here is a messy retail sales dataset. Transform it so it is clean and ready for downstream use. - How would you handle missing values, inconsistent formats, and duplicate records? They gave me a hypothetical retail sales report with missing values, inconsistent formats, and duplicate records. The prompt was pretty ambiguous, basically just to make it clean and reproducible for downstream use, so I used Python, mostly pandas, to wrangle it into a better structure. The dataset itself was not huge, more mid-sized, but the lack of detailed instructions made it harder.