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Interview Process Analysis: AI Engineering Job Market

Summary Statistics

Out of 1,765 job descriptions analyzed, only ~80 (~4.5%) include a structured interview process across 51 unique companies. The vast majority either omit the process entirely.

Individual interview process descriptions for each of the 51 companies are in data/job-descriptions/. Each file links to the source job description YAML.

Interview Process

The median process has 4 steps, with most companies falling in the 3-5 range. A few lean processes have just 2 stages (Lorikeet, Infinity Constellation, Watershed), while the longest reach 7 stages (FlowFuse, Roboflow, The College Board).

Most frequently mentioned steps:

  1. Recruiter/talent screen - Usually 15-30 min
  2. Technical interview - Live coding, system design, or code review
  3. Hiring manager interview - 45-60 min deep dive
  4. Behavioral interview - Interview about values and culture
  5. Take-home challenge - Typically 2-3 hours
  6. Panel interview - Multiple interviewers
  7. CEO/founder interview - Usually final step, 15-30 min

Examples from job postings (data/job-descriptions/):

Doctolib, Senior AI Engineer:

  1. Recruiter interview
  2. Feature building interview
  3. AI system design interview
  4. Behavioral interview
  5. Reference check and offer

PostHog, AI Product Engineer:

  1. Talent partner call
  2. Technical interview (60 min)
  3. Co-founder call (15 min)
  4. Paid SuperDay (full day of actual work, compensated)

FlowFuse, Full Stack Developer (AI):

  1. Resume review by hiring manager
  2. Screening call (15 min)
  3. Engineering manager call (45 min)
  4. Take-home assignment (2-3 hours, AI tools encouraged)
  5. Technical review with 2-3 team members (60 min)
  6. Team interview on collaboration and communication (45 min)

What Candidates Actually Experience

Candidate reports from Reddit, X, and personal blogs confirm our data and add detail on what each round looks like in practice:

Round Duration What people report
Recruiter screen 15-30 min Basic fit, salary expectations
Technical/coding 45-60 min LeetCode-style, sometimes AI-flavored
AI/ML deep-dive 45-90 min LLMs, RAG, hallucinations, fine-tuning vs prompting
Take-home/project 1-7 days Build RAG/agent system, or multi-day assignment
System design 60 min Scale LLM apps, cost/latency optimization
Behavioral 30-60 min STAR format, ownership in ambiguous AI work

Not every company includes all rounds. Total: 3-6 rounds, 2-6 weeks.

Microsoft, SWE Applied AI/ML Intern 1:

  1. AI-assisted coding (45 min) - use ChatGPT to solve problems, interviewer modifies problem and asks to re-prompt
  2. Raw coding, no AI tools allowed (45 min)
  3. Behavioral/technical discussion (45 min)

Amazon, GenAI Innovation Center L6 2:

  1. Phone screen: LeetCode problem + practical ML coding question (cosine similarity in NumPy)
  2. Standard SDE technical bar (DSA coding) - no dedicated MLE job family
  3. GenAI depth: LLM/ViT/DiT architectures, fine-tuning, use case ideation, ROI estimation
  4. Leadership Principles (LP) behavioral questions throughout

Eightfold.ai, Agentic AI Engineer 3 4 (Jan 2026):

  1. AI agent-conducted coding round (~60 min) - 2 questions with interactive follow-ups on edge cases and complexity
  2. Take-home: 3-day assignment to build an AI agent
  3. DSA-focused technical interview with engineering manager

LangChain, AI Engineer 5:

  1. Take-home assessment (develop an agent)
  2. Discussion of the solution
  3. Applied system design interview

IBM, AI Engineer (Watsonx) 6 (Jan 2025):

  1. Recruiter screen (applied via LinkedIn, ~2 months wait)
  2. Technical interview (75 min) - Python, SQL, Git, project deep-dives, ML/MLOps
  3. Live coding (45 min) - shared whiteboard, 3 questions (easy to medium)

Mistral AI, Applied AI Engineer 7 (Jan 2026):

  1. LLM theory
  2. Coding
  3. Past project deep-dive
  4. Technical manager interview
  5. ML system design
  6. Take-home assignment
  7. Value talk

Databricks, AI/ML Engineer 8 (late 2025):

  1. Coding (LeetCode-style)
  2. Multi-level OOP (scale toy systems like DB/KV store/chat room)
  3. ML infra design (feature stores, distributed training, serving)
  4. Pre-offer reference checks (2-3 refs required)

Goldman Sachs, Applied AI Engineer 9 (Dec 2025):

  1. Technical interview for GenAI/applied AI role
  2. Focus on LLM system design and production deployment

AI Engineer Intern 10 (Jan 2026):

  1. Deep dive on resume projects
  2. QLoRA fine-tuning
  3. RAG architecture and latency optimization
  4. Temperature and sampling strategies
  5. Agentic AI system design
  6. Feature scaling and ML situational cases

GenAI Engineer, product company 11 (2025):

  1. Technical interview focused on GenAI engineering
  2. LLM system design, RAG tradeoffs, evaluation metrics beyond perplexity

Footnotes

  1. Reddit - Microsoft SWE Applied AI/ML Summer 2026 (r/csMajors) ↩

  2. Reddit - ML Engineer GenAI Amazon (r/datascience) ↩

  3. Medium - Inside Eightfold.ai Agentic AI Internship Hiring 2026 ↩

  4. Reddit - Need Advice for Eightfold.ai Agentic AI Engineer (r/developersIndia) ↩

  5. Reddit - AI Engineer Interview Questions (r/ArtificialIntelligence) ↩

  6. Medium - Raghu Teja, IBM Part 1 ↩

  7. Glassdoor - Mistral AI Applied AI Engineer Interviews ↩

  8. Yuan Meng - MLE Interviews 2.0 ↩

  9. Reddit - Applied AI Engineer Goldman Sachs Interview (r/leetcode) ↩

  10. X - AI Engineer Intern Interview ↩

  11. Reddit - Technical Interview for GenAI Engineer Role (r/leetcode) ↩