25 Best Internship Interview Questions to Ask & Answer
By
Ethan Fahey
•
Jan 29, 2026
Internship interviews for AI engineers, ML researchers, data scientists, and infra developers don’t look anything like they used to. Casual chats about classes and interests have been replaced by structured, multi-stage processes that mix coding challenges, system thinking, and behavioral interviews. With remote and hybrid teams now the norm, AI-driven tools often handle early screening, resume review, and even parts of interview analysis. The key mindset shift is realizing this goes both ways: teams are assessing your technical depth, learning speed, and collaboration style, while you should be evaluating their culture, mentorship quality, and whether their AI work is actually production-ready and ethically sound or just good branding.
Most growth-stage AI startups now rely on structured interviews, take-home tasks, and automated screening, which means preparation matters more than ever. Strong candidates don’t just solve problems; they explain tradeoffs, communicate clearly, and show they’ve thought about how they want to grow.
Key Takeaways
Asking strong, targeted questions in an internship interview helps you evaluate the role, team dynamics, and how a company uses AI responsibly, with critical factors for AI, ML, and engineering roles in 2025–2026.
This article covers 25 specific internship interview questions: 15 common interview questions interviewers may ask you (with guidance on strong answers) and 10 smart questions you should ask them.
AI is reshaping the hiring process through automated screening, assessments, and scheduling, but Fonzi AI uses bias-audited, candidate-friendly tools that create clarity instead of confusion.
Fonzi’s Match Day gives AI and software engineers a high-signal, 48-hour hiring window with salary transparency, pre-vetted roles, and concierge support to compress what typically takes 30–45 days.
Quick Reference: 25 Best Internship Interview Questions
Before diving into detailed explanations, here’s your cheat sheet of all 25 questions. Use this for quick reference before any job interview or Match Day session.
Questions Interviewers Often Ask You (1–15):
Can you walk me through a recent technical project you’re proud of?
Why are you interested in this internship and this specific team?
What are your strongest technical skills today, and what are you actively improving?
Tell me about a time you had to learn a new technology quickly.
How do you approach debugging a tricky bug or failing model?
Describe a time you worked on a team project. What role did you play?
How do you prioritize tasks when you have multiple deadlines?
How do you like to receive feedback, especially on code or models?
Tell me about a mistake you made on a project and what you learned from it.
What do you hope to learn during this internship, technically and professionally?
How would you evaluate whether an ML model is “good enough” for production?
What potential risks or biases should you consider when building AI systems?
How do you stay current with rapid changes in AI and software engineering?
Tell me about a time you disagreed with a teammate on a technical decision.
What does working on a high-performing engineering team look like to you?
Questions You Should Ask Them (16–25):
How does your team currently use AI in the product and internally in the development process?
What will my first 60–90 days look like in terms of projects and onboarding?
How is intern performance evaluated, and how often will I get feedback?
What mentorship or pairing opportunities exist for interns?
What tools and workflows does the team use day to day?
How do you make sure AI is used responsibly here?
What does success look like for interns by the end of the program?
How often have past interns converted to full-time roles, and what did that path look like?
What do you personally enjoy most about working here, and what challenges do you see?
What are the next steps and expected timeline after this interview?
The sections below expand on how to answer these questions (for candidates) and how to interpret answers (for hiring managers), using examples from typical Fonzi Match Day conversations.
Core Internship Interview Questions You’ll Be Asked (and How to Answer)
These 10 questions form the backbone of most AI and engineering internship interviews in 2025–2026, whether conducted live, remote, or asynchronously. Your answers should blend technical skills (languages, frameworks, math, systems) with evidence of learning, collaboration, and ethics, especially if you’ll be working with production AI systems.
Examples can come from university work after 2021, open-source contributions on GitHub, Kaggle competitions, hackathons, or research projects. A candidate who built an LLM fine-tuning project or contributed to a distributed training pipeline has excellent material to draw from.
Each question below includes what the interviewer is really looking for, how a strong candidate might structure a response using the star method, and a quick example scenario. These questions closely mirror what Fonzi partner companies ask during Match Day for intern and early-career roles.
“Can you walk me through a recent technical project you’re proud of?”
What they’re looking for: Clarity of thinking, ownership, and basic familiarity with tools.
How to answer: Choose one specific project from 2022–2025 and tell a concise story: problem, tech stack, decisions, and results. For example, building a recommendation system in Python or deploying a small LLM-based chatbot using OpenAI’s API.
Structure your answer to this question using the STAR format:
Situation: Set the context briefly
Task: What you needed to accomplish
Action: Technical decisions you made and why
Result: Quantifiable outcomes
Include at least one trade-off discussion, such as accuracy vs. inference speed, or consistency vs. availability in a distributed system. Quantify outcomes when possible: “reduced page load time by 30%” or “improved F1 score from 0.74 to 0.82.”
“Why are you interested in this internship and this specific team?”
What they’re looking for: Evidence you’ve researched the company’s mission and can articulate genuine interest.
How to answer: Name concrete aspects of the company, such as their work on multimodal models, privacy-preserving ML, or real-time recommendations instead of generic enthusiasm. Connect your background (courses, side projects, research) to the team’s current roadmap or tech stack.
Reference how the company uses AI responsibly (model explainability, bias audits, or safety reviews) as an extra signal of alignment with their company’s values. Fonzi profiles often include these details upfront, making it easier for candidates to give targeted answers on Match Day.
“What are your strongest technical skills today, and what are you actively improving?”
What they’re looking for: Self-awareness and a learning plan.
How to answer: Pick 2–3 concrete strengths (e.g., “I’m strongest in Python, PyTorch, and data wrangling with Pandas”) and 1–2 honest growth areas (distributed systems design, TypeScript, or prompt engineering for LLMs).
Use specific examples: “In Fall 2024, I completed a project fine-tuning a BERT model for sentiment analysis; now I’m learning about retrieval-augmented generation for LLMs.” This shows you’re genuinely interested in career development and acquiring new skills.
“Tell me about a time you had to learn a new technology quickly.”
What they’re looking for: Learning velocity and resourcefulness.
How to answer: Pick a scenario-specific example; picking up Docker for a class project's deadline in March 2023, or learning LangChain in 2024 to prototype a retrieval-augmented chatbot.
Focus on your learning process: documentation, mentors, experimentation, and debugging. Mention any constraints (tight deadlines, limited guidance) and what you’d do differently next time. AI and infra stacks evolve every 6–12 months, so this question assesses whether you can keep pace.
“How do you approach debugging a tricky bug or failing model?”
What they’re looking for: Calm, systematic reasoning rather than guesswork.
How to answer: Outline a step-by-step thought process: reproduce, isolate, hypothesize, test, verify. Use a concrete example like exploding gradients during model training or a flaky integration test in CI.
Include tools (logging, unit tests, TensorBoard, profilers, or observability tools like Grafana) and collaboration habits (posting minimal repros, asking mentors smart questions). A structured debugging mindset is explicitly evaluated in machine-learning and infra-focused internships.
“Describe a time you worked on a team project. What role did you play?”
What they’re looking for: Communication habits, conflict resolution, and willingness to take unglamorous tasks.
How to answer: Cite a specific team experience after 2021; a university capstone, hackathon, or group project with other interns. Describe your communication patterns (standups, docs, code reviews) and how you kept team members on the same page.
Mention tooling like GitHub, Jira, Notion, or Slack. Show willingness to handle testing, documentation, or cleanup. Even solo-heavy AI work requires collaboration with product, design, and infra, so this matters for all roles.
“How do you prioritize tasks when you have multiple deadlines?”
What they’re looking for: Time management skills and proactive communication.
How to answer: Mix practical tactics (using Notion, Google Calendar, or Kanban boards) with examples like balancing finals week in May 2024 with a research assistantship or part-time job.
Highlight proactive communication: renegotiating deadlines early, flagging risks in standups. Match Day processes at Fonzi often highlight interns who can clearly communicate their timelines and constraints when handling multiple tasks.
“How do you like to receive feedback, especially on code or models?”
What they’re looking for: Low-ego collaboration and coachability.
How to answer: Show openness to both written and live feedback with code reviews on GitHub, pair-programming sessions, and model evaluation discussions.
Mention a specific time constructive criticism led to improvement: “A 2023 PR review changed my approach to testing and model documentation.” Fonzi emphasizes feedback-rich, bias-audited evaluation rubrics rather than ad-hoc, personality-based judgments, and this question tests whether you’ll thrive in that environment.
“Tell me about a mistake you made on a project and what you learned from it.”
What they’re looking for: Ownership, transparency, and process improvement.
How to answer: Pick a real but safe example, such as accidentally committing secrets, mislabeling training data, misconfiguring a cloud resource, and show responsible recovery.
Emphasize telling the team quickly and implementing concrete process changes (checklists, linters, CI rules). For AI internships, this might include lessons about evaluation leaks, data privacy, or model misuse. Fonzi partner companies prefer candidates who discuss failure thoughtfully rather than claiming perfection.
“What do you hope to learn during this internship, technically and professionally?”
What they’re looking for: Alignment between their roadmap and your career goals.
How to answer: Name specific technologies or domains: “productionizing LLMs with retrieval-augmented generation,” “MLOps on Kubernetes,” “building robust REST APIs with Go.” Add softer skills like working with PMs or writing design docs.
Mention long-term aspirations (becoming an ML engineer in 2027 or a staff-level infra engineer later) without overpromising. Fonzi profiles often include “learning goals,” which help startups propose higher-signal projects for interns during Match Day.
Advanced & AI-Specific Internship Questions (Ethics, Impact, and 2026 Realities)
Beyond standard behavioral prompts, these deeper questions increasingly appear for internships in AI, ML, and data science. They probe how you think about AI safety, bias, evaluation, and real-world consequences.
Companies using Fonzi frequently work on sensitive domains—healthcare, finance, hiring, or security—where these topics matter even for interns. Each question focuses on interpretability and ethical reasoning, though examples include real techniques like fairness metrics, A/B tests, and guardrails for LLM outputs.
“How would you evaluate whether an ML model is ‘good enough’ for production?”
What they’re looking for: Understanding of both offline metrics and real-world criteria.
How to answer: Mention offline metrics (accuracy, F1, ROC-AUC, calibration) alongside business or safety criteria. Give a concrete example, like evaluating a 2024 fraud detection model or recommendation system, and discuss trade-offs between precision and recall.
Show awareness of test sets, drift, and ongoing monitoring. You don’t need perfect answers, but demonstrate that you understand the job entails more than just model accuracy.
“What potential risks or biases should you consider when building AI systems?”
What they’re looking for: Basic ethical literacy and awareness of societal impact.
How to answer: Cover dataset bias, representativeness, privacy, and potential misuse, especially in hiring, lending, or healthcare. Reference real public incidents—biased facial recognition or unfair hiring algorithms exposed around 2020–2022.
This connects directly to Fonzi’s commitment to bias-audited evaluation in our own hiring workflows and candidate scoring. It shows you understand AI’s broader implications.
“How do you stay current with rapid changes in AI and software engineering?”
What they’re looking for: Curiosity, discipline, and hype-filtering ability.
How to answer: Reference specific sources and routines: reading arXiv or Papers With Code, following newsletters since 2023, joining Discord or Slack communities, and contributing to open-source.
Mention how you decide what to ignore (filtering hype) versus what to actually implement or test. Fonzi shares curated, role-specific prep material with candidates before Match Day to accelerate this learning.
Smart Questions to Ask Your Interviewer as an Intern (Including About AI Use)
The questions you ask at the end of an interview strongly shape how the team perceives you and how you judge the opportunity. Thoughtful questions signal that you’re evaluating fit just as carefully as they are.
This section focuses on 10 high-signal questions, including several about AI integration, mentorship, performance review, and conversion to full-time roles. Each includes why it matters, what a good answer looks like, and what might be cause for concern.
These questions to ask are especially relevant for remote-first or hybrid AI teams using modern tooling like GitHub Copilot, internal LLMs, or interview automation.
“How does your team currently use AI in the product and internally in the development process?”
Why it matters: Distinguishes AI as a core capability versus a buzzword.
Good answer looks like: Specific systems or workflows, like LLMs assisting code reviews with humans approving decisions, recommendation engines shipped in 2024–2025, or anomaly detection in production.
Cause for concern: Vague, hand-wavy responses suggest the company is still figuring things out or overselling their AI story. Fonzi partner companies are vetted for serious technical work rather than hype-driven “AI-washing.”
“What will my first 60–90 days look like in terms of projects and onboarding?”
Why it matters: Reveals whether your internship experience will be structured or chaotic.
Good answer looks like: Named projects or components, expected deliverables, and a documented onboarding process with intro docs, starter tickets, and buddy systems.
Cause for concern: “We’ll figure something out later” with no clear owner or mentorship plan. Fonzi companies often share example 30–60–90 day plans during Match Day pitches.
“How is intern performance evaluated, and how often will I get feedback?”
Why it matters: Clarifies expectations and ensures you won’t be blindsided.
Good answer looks like: Weekly 1:1s, mid-internship review, final review with written rubrics and example goals. Strong teams articulate clear evaluation criteria.
Cause for concern: The interviewer can’t explain any evaluation process or seems surprised by the question. Fonzi uses structured, bias-audited scoring to encourage fairness and clear communication.
“What mentorship or pairing opportunities exist for interns?”
Why it matters: Distinguishes resume-building internships from genuine personal development experiences.
Good answer looks like: Assigned mentors, regular pairing sessions, internal tech talks, and access to staff/principal engineers.
How to interpret: A lean 5-person startup might offer direct founder time but little formal structure; a larger org might have a defined internship program with recurring workshops. Both can work, just know what you’re signing up for.
“What tools and workflows does the team use day to day?”
Why it matters: Helps you understand what relevant experience you’ll gain.
What to listen for: Code hosts (GitHub, GitLab), CI/CD tools, cloud platforms (AWS, GCP, Azure), experiment tracking (Weights & Biases, MLflow), and communication tools.
Very ad-hoc or outdated workflows might mean more chaos, but also opportunities to contribute process improvements. Fonzi candidate profiles often include preferred stacks for advanced matching.
“How do you make sure AI is used responsibly here?”
Why it matters: Tests whether ethics is built into their process or an afterthought.
Good answer looks like: Model review processes, data privacy practices, human oversight, internal guidelines created in 2023–2025, or reviews led by a responsible AI committee.
Cause for concern: “We just ship fast and fix later” is a red flag for sensitive domains. Fonzi’s core message: AI should increase clarity and fairness, not replace human responsibility.
“What does success look like for interns by the end of the program?”
Why it matters: Helps gauge whether you’ll handle meaningful work or only low-impact tasks.
Good answer looks like: Shipped features, merged pull requests, production-impacting experiments, documented research findings. Strong teams give examples from the summer 2023 or 2024 interns.
Fonzi asks partner companies to define success outcomes upfront for each internship position.
“How often have past interns converted to full-time roles, and what did that path look like?”
Why it matters: Indicates whether the company invests in career path development.
Good answer looks like: Precise conversion rates (“In 2023 and 2024, 40–60% of software interns joined full-time”), typical timeframes, and clear criteria.
For remote-first environments, ask how they onboarded interns permanently across time zones. This question doesn’t make you sound demanding; it shows you think in terms of long-term impact.
“What do you personally enjoy most about working here, and what challenges do you see?”
Why it matters: Makes the interview more human and reveals authentic team dynamics.
What to listen for: Hesitation or overly polished answers. Compare different interviewers’ responses across rounds to spot patterns.
Fonzi encourages candidates to debrief with us after interviews to interpret these qualitative signals together.
“What are the next steps and expected timeline after this interview?”
Why it matters: Reduces anxiety and helps you plan across multiple processes.
Good answer looks like: Clarity on additional rounds (technical, manager, take-home) and target decision dates.
In Fonzi’s Match Day, timelines are intentionally compressed, such as within a 48-hour decision window, to avoid extended uncertainty. Send a specific, personalized thank-you email within 24 hours that references a topic from the conversation.
How AI Is Changing Internship Interviews (and How Fonzi Uses It Differently)
AI now touches nearly every stage of the hiring process. Resume parsing filters applications before humans see them. Coding assessments run through automated graders. Video interview platforms analyze speech patterns for confidence indicators. ATS systems score candidates against job requirements using keyword matching.
For candidates, this creates real concerns: opaque scoring systems, potential bias in automated evaluations, feeling like you’re talking to bots instead of humans, and long, unclear timelines while algorithms make decisions behind the scenes.
Fonzi approaches this differently. We use AI to surface matches, detect fraud, and automate logistics, but humans remain in the loop for all decisions. Our evaluations are bias-audited and rubric-based, not black-box predictions. When a candidate uploads their GitHub and recent ML projects, AI helps organize and tag skills, but human talent specialists review and refine each profile.
The goal is transparency, not replacement. Salary bands are shared upfront with every job description. The interview process expectations are clear before you commit time. This matters because experienced professionals and fresh perspectives alike deserve clarity. AI should help recruiters focus on people and potential, not replace human judgment on who belongs on a team.
10 Sample Questions, What Interviewers Learn, and How You Benefit
This table pairs 10 high-impact questions with what they reveal and how they help you make an informed decision about whether an internship is right.
Question | What the Interviewer Learns | How It Helps You as a Candidate |
Walk me through a recent technical project | Your depth, ownership, and ability to communicate technical decisions | Shows if they value your own experience and specific skills listed |
Why this team and this internship? | Whether you’ve researched the company goals and show genuine interest | Reveals if the job role matches your expectations |
What’s your debugging process? | Problem solving approach and systematic thinking | Indicates if they value methodical approaches over heroics |
How does your team use AI in the product? | Your curiosity about their technical maturity | Exposes whether AI claims are real or marketing |
What will my first 60–90 days look like? | Your focus on ramp-up and delivering impact | Reveals onboarding process quality and structure |
How is intern performance evaluated? | Your concern for growth opportunities and feedback | Shows whether employee satisfaction and development matter |
What mentorship exists for interns? | Your prioritization of learning and collaboration | Indicates investment in career development |
How do you ensure responsible AI use? | Your awareness of ethics and safety | Reveals if they take AI responsibility seriously |
What does intern success look like? | Your outcome-orientation and ambition | Clarifies whether you’ll do meaningful work |
How often do interns convert to full-time? | Your long-term thinking and career goals | Shows conversion rates and what the career path looks like |
Use this as a quick checklist before each interview or Match Day session. Print it, save it, and review it to ensure you’re covering the questions that matter most for your internship experience.
Preparing for Your Internship Interview: Practical Steps for AI & Engineering Roles
Preparation separates strong candidates from everyone else. Here’s a practical checklist:
Update your GitHub with recent commits. Pin 2–3 repositories that showcase your best work with clear READMEs.
Polish 1–2 project stories using the star method. Practice telling them in under 3 minutes with quantifiable results.
Revisit fundamentals. Review data structures, algorithms, and system design basics. For ML roles, brush up on loss functions, regularization, and evaluation metrics.
Draft 5–7 questions from this article tailored to each company. Reference specific details from their tech blog or product pages.
Simulate real conditions. Solve LeetCode-style problems under time pressure. Practice whiteboarding system designs. Explain an ML pipeline to a non-technical friend.
Research each company in the week before interviews. Note at least three specific facts or questions per company, including products, team members, and recent announcements.
Check job requirements against your experience. Be ready to address gaps honestly and show your learning plan.
Create a short “interview journal” to track which questions worked well and which answers you want to refine. This builds momentum across multiple processes and helps you achieve success faster.
Conclusion
Great internship interviews feel less like a pop quiz and more like a two-way conversation about fit and impact. For AI, ML, and engineering roles, the questions you ask around ethics, evaluation practices, tooling, and mentorship signal just as much as your technical skills. Curiosity and judgment matter, especially in business contexts where AI decisions quickly turn into real products and real risk.
The 25 questions here are meant to give candidates a practical framework, whether you’re breaking into AI from another role, landing your first technical internship, or looking for a higher-signal team to grow with. They help surface how a company actually works, how it develops talent, and whether the role will meaningfully expand your skills and network.
