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Anthropic Interview | ML Engineer | November 2025

Interview rounds

  1. Recruiter Screen - Initial screening call
  2. Technical Assessment - Take-home or asynchronous assessment
  3. First Technical Interview - Live coding session
  4. Panel Interview - Work simulation with 3 interviewers
  5. Final On-site Round - Two parts:
    • Python Coding Challenge
    • Debugging Test
  6. Hiring Manager Round

Interview questions by round

Recruiter screen

  • Questions about understanding basic fit, certifications, and resume content
  • Any remote courses or in-person courses completed?
  • Formal education relating to machine learning engineering roles
  • Prior work experience
  • Volunteering experience

Technical assessment (Take-home)

Main Prompt: Extract a clean data set from this database using only the AI prompt on the task (using SQL and Python)

Details:

  • Given a demo data set from the company
  • Use only SQL and Python
  • Clean and extract data
  • Ethics questions asked:
    • Were the data sets properly formatted?
    • Was the format compliant with regulatory levels?
    • Can this data be used in its current state for deep visualizations or deep analysis tasks?
    • Could it be used for third-party analytical settings?

Difficulty: Candidate described it as "a bit technical" and more complex than typical online course tests, but doable

First technical interview (Live coding)

Main Prompt: Pull data from company database using only Python with no external NLP packages (no spaCy, no Hugging Face)

Tasks:

  • Data cleaning from the original file extracted
  • More analysis in Python using pandas and numpy
  • Limited to Python only, no third-party add-ons

Focus difference from first assessment:

  • First assessment: More about pulling/extracting data
  • This round: More about cleaning the data that was pulled

Ethics questions:

  • AI alignment
  • Human impact
  • Decision accountability

Note: Candidate noted Anthropic asked more about ethics than typical ML roles - moved from qualifications/credentials to ethics and standards

Panel interview (3 interviewers - work simulation)

Format: Not a strict coding task, but a real-world work simulation using SQL and Python only

Main Scenario Prompt: "Imagine you're part of a team deploying a conversational AI model that can reason across sensitive topics. During internal testing, you discovered that the model usually gives overly confident but factually wrong answers in high-risk contexts. How would you investigate and mitigate/solve that problem?"

Structure:

  1. Part 1: Walk through results from the two previous tech screens (extraction and cleaning)
  2. Part 2: Work through the scenario prompt
  3. Part 3: Answer follow-up questions in three areas:
    • Model misuse scenarios (First interviewer)
    • Alignment and credibility (Second interviewer)
    • Data ethics and privacy (Third interviewer)

Tools allowed: SQL and Python only

Final on-site - Python coding challenge

Main Prompt: "Here's a data set - transform it so it's clean and ready for downstream use"

Data Set Details:

  • Sales data set/report for a hypothetical retail company
  • 59 rows × 1500 columns
  • Mid-size enterprise format
  • Messy data with:
    • Missing values
    • Inconsistent data formats
    • Duplicated records

Task characteristics:

  • Very ambiguous prompt - no super detailed instructions
  • Goal: Demonstrate data wrangling, logical structuring, and reproducible transformation
  • Used Python with pandas
  • Had to write and optimize a few functions
  • Data transformation and model preprocessing tasks (things you'd do in real-world ML workflow)

Note: Not like LeetCode-type problems - more practical/real-world oriented

Final on-site - debugging test

Setup: Pre-written Python script provided - a data pipeline for model training

Main Prompt: "Find what's making the scripts run incorrectly and fix it"

Details:

  • They hinted there were two bugs
  • But also said: if you find more bugs along the way, fix those too
  • Went in "blind" without all the details
  • Script size: 198 lines of Python code

Candidate's outcome: Ran out of time and did not complete this round, which is why they didn't proceed to the hiring manager round

Key themes

Heavy ethics emphasis: Throughout the entire process, Anthropic consistently asked about:

  • AI safety
  • Ethical considerations
  • Regulatory compliance
  • Human impact
  • Decision accountability
  • Data privacy and ethics

Practical vs. theoretical: Questions were more about real-world scenarios and practical data work rather than algorithmic/LeetCode-style problems

Ambiguous prompts: Many prompts were intentionally vague to test how candidates handle ambiguity and structure their own approach

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