INTRODUCTION - The Night Before the AI Interview Is Where Performance Begins

Every candidate thinks the hardest part of an AI or ML interview is the interview itself, the algorithms, the modeling tradeoffs, the ambiguity of system design, the pressure of thinking aloud while an engineer silently evaluates your reasoning. But top performers know this isn’t true.

The real interview begins the night before.

That’s when your cognitive state is set, your mental models stabilize, your memory reorganizes, and your nervous system decides whether it will support you, or sabotage you. It is the difference between walking into an interview with quiet clarity versus walking in with frantic mental noise. It is the difference between sounding intentional, structured, senior… and sounding chaotic, rushed, reactive.

ML interviews aren’t exams; they’re performances. And like any performance, elite athletes, researchers defending a paper, founders pitching investors, success depends on your pre-performance ritual. How you prepare in the final 12 hours has disproportionate impact on your clarity, energy, confidence, and cognitive endurance.

This blog is not a generic checklist. It is a psychological, strategic, and technical preparation system used by top candidates who consistently do well in ML/AI interviews. We’ll break these ideas into five deep sections:

  1. What to revise (and not revise) the night before
  2. How to rehearse your reasoning without burning mental energy
  3. What to check technically for online interviews
  4. How to prime your brain for clarity and reduce cognitive load
  5. A complete, high-signal checklist you can follow every time

By the end, you’ll have a repeatable night-before ritual that sharpens your thinking, calms your nerves, and maximizes your performance, whether you're interviewing at FAANG, OpenAI, DeepMind, Anthropic, Tesla, or fast-moving AI startups.

 

SECTION 1 - What to Revise the Night Before (And Why Most Candidates Over-Prepare)

The night before your AI interview is not the time to learn something new. Yet most candidates make the same fatal mistake: they “cram.” They watch ML system design videos until midnight. They skim transformer attention math. They reread every notebook they’ve ever written. They dump 200 flashcards into their brain hoping the interviewer will magically ask about those exact concepts.

But the night before an ML interview should not build knowledge, it should stabilize cognition.

Your goal is not to get smarter.
Your goal is to get clearer.

You don’t sharpen a sword in the final minutes before battle —
you clean it, steady it, and prepare* it.

Here’s what top ML candidates revise, and why.

 

1. Review your mental scaffolds, not your memory

Strong ML candidates rely on thinking structures, not memorized answers. These structures allow them to navigate uncertainty, adapt when interviewers add constraints, and reason clearly even when they don’t immediately know the answer.

The night before, they review only:

  • Their ML system design framework (not the details)
  • Their model selection logic
  • Their evaluation + metrics reasoning template
  • Their tradeoff playbook
  • Their data reasoning checklist

These are lightweight, mental “containers” that guide your thinking during the interview.

This approach mirrors what strong candidates do in framing-heavy interviews, described well in:
➡️The Forgotten Round: How to Ace the Recruiter Screen in ML Interviews

By reviewing scaffolds instead of content, you avoid cognitive overload and improve clarity.

 

2. Review 2–3 of your strongest projects (not dozens)

Interviewers will almost always ask:

  • “Tell me about a past ML project.”
  • “What tradeoffs did you face?”
  • “What challenges did you handle?”

Instead of revisiting 10 different projects and drowning in details, choose:

  • One end-to-end ML project
  • One optimization/experimental project
  • One failure or “learning” project

Review ONLY:

  • the problem framing
  • key design decisions
  • tradeoffs
  • failure modes
  • impact

This preparation helps you sound polished without sounding rehearsed.

 

3. Skim 3–4 recently solved practice questions

Not to learn them, but to reactivate your reasoning pathways.

Reviewing solved problems reminds your brain:

  • how to structure answers
  • how to talk through ambiguity
  • how to narrate tradeoffs
  • how to simplify complex ideas

You’re priming your cognitive engine, not feeding it new fuel.

 

4. Create a 2-minute “narrative snapshot” for yourself

This is a concise mental summary of:

  • Who you are
  • Why you’re transitioning / interviewing
  • What roles you’re targeting
  • What your strongest area is
  • What your “through-line” story is

Interviewers remember candidates with clean narratives.
They forget candidates who talk in circles.

 

5. Stop revising anything new after 9–10 PM

Late-night cramming elevates cortisol, reduces memory retrieval accuracy, and increases the risk of panicked overthinking. Your clarity tomorrow depends on your calmness tonight.

Avoid:

  • memorizing definitions
  • reading research papers
  • digging into code
  • solving new, unfamiliar problems

The night before an interview is about mental sharpness, not mental volume.

 

6. Write down things you might forget

Your brain gets calmer when it stops trying to remember everything.
Write down:

  • interviewer names
  • interview type (coding / design / behavioral)
  • what you want to highlight
  • what you want to avoid saying
  • any specific questions you want to ask them

This eliminates mental clutter and frees cognitive bandwidth.

 

7. Rehearse calmness, not cleverness

Strong candidates don’t rehearse answers.
They rehearse states.

A calm candidate → thinks clearly
A clear candidate → structures well
A structured candidate → sounds senior
A senior-sounding candidate → gets hired

The goal is not to sound brilliant.
The goal is to sound balanced.

 

SECTION 2 - The Cognitive Reset: How to Prepare Your Mind the Night Before an AI/ML Interview

There is moment candidates rarely talk about openly, but every ML engineer has felt it. It’s the night before the interview, when the preparation window is closing, when adrenaline meets uncertainty, and when the mind starts spinning faster than you want it to. It’s the moment when your browser tabs remain open even though your productivity has vanished. It’s the moment when overthinking starts masquerading as preparation.

What you do tonight matters, not because you will magically acquire new knowledge, but because tonight determines the state of mind you walk in with tomorrow. And in ML interviews, your state of mind is as important as your technical skill.

ML interviews measure structured reasoning, calmness under pressure, clarity of explanation, and the ability to think aloud while navigating ambiguity. None of those abilities show up when your brain is exhausted, scattered, or running on anxiety loops. That is why the night before the interview is not about studying harder. It’s about resetting, clearing mental bandwidth so your cognitive systems function at full capacity.

This section explores how top candidates prepare their mind the night before: not through memorization, but through intentional mental conditioning that strengthens clarity, presence, and composure.

 

The Night Before Is Not for Learning-It’s for Stability

There is a dangerous trap many candidates fall into: believing that last-minute studying will give them an edge. In reality, neural fatigue and cognitive overload sabotage the exact skills ML interviews measure. You don’t think more clearly when you’re exhausted. You don’t reason more effectively when you’re mentally cluttered. You don’t frame problems better when your head is filled with noise.

Strong candidates treat the night before like an athlete treats the night before a competition:
Not as a time to train, but as a time to stabilize.

What does stability look like?

  • A calm, familiar mental rhythm
  • A sense of control over your environment
  • A clear, coherent mind
  • A predictable cognitive state
  • A body that feels rested, not frantic

Stability is a performance multiplier. Tomorrow, the interviewer won’t see how many hours you studied, they will see whether your brain is operating smoothly.

 

Do a Light Mental Rehearsal—Not a Study Session

Instead of trying to force more content into your head, top performers do a light rehearsal. Not solving new problems. Not reading new theory. Just reminding the brain of the architecture it already knows.

The tiny run-through helps reinforce:

  • Your problem-framing structure
  • Your thinking-aloud rhythm
  • Your system design flow
  • Your approach to evaluating tradeoffs
  • Your fallback methods when stuck

It’s not learning.
It’s priming.
And priming creates consistency.

For example, some candidates spend 15 minutes reviewing their go-to frameworks:

For ML system design:

  • Framing
  • Data
  • Modeling
  • Evaluation
  • Deployment
  • Failure modes
  • Monitoring

For modeling questions:

  • Assumptions
  • Baselines
  • Feature families
  • Modeling direction
  • Metrics
  • Tradeoffs

These frameworks are not meant to be memorized, they are scaffolds your brain will lean on when pressure peaks.

Light rehearsals resemble the mental warm-up strategies used by top ML interviewees, described in resources like:
➡️The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code

This is all your brain needs tonight, not new knowledge, but a reminder of its existing structure.

 

Reduce Cognitive Load: Clear What Pulls Attention Away

A cluttered environment leads to a cluttered mind. The night before an interview, even small sensory distractions amplify stress:

  • Too many browser tabs
  • A messy desk
  • Notifications popping up
  • Unfinished tasks
  • Errands you postponed
  • Family or social obligations you didn’t acknowledge

Your brain cannot relax when it feels something is “unresolved.”

Research on cognitive load shows that unfinished tasks act like mental parasites—they consume processing power, even when you’re not consciously thinking about them. Top performers use the night before to reduce this load by closing loops:

  • Writing down any lingering tasks so the brain stops holding them
  • Cleaning the workspace where the online interview will happen
  • Closing all unnecessary apps
  • Turning off notifications
  • Setting default do-not-disturb modes
  • Ensuring the interview environment is predictable

The goal is simple:
When your interview begins, the only thing your mind should think about is the problem in front of you, not your surroundings.

 

Regulate Your Nervous System Before Sleep

Many candidates go to bed anxious. Their mind replays “what-if” scenarios. They feel tension in their chest. They review every potential failure mode. And the next morning, they wake up exhausted, even if they slept eight hours.

Research athletes handle this differently.

Elite-level performers regulate the nervous system the night before through:

  • Slow breathing (4–6 cycles per minute)
  • Light stretching to reduce muscle tension
  • A warm shower to activate parasympathetic calm
  • 10–15 minutes of mindfulness or guided focus
  • A brief walk to reset mental noise

These activities quiet the emotional brain and activate the prefrontal cortex, which is responsible for logical thinking, reasoning, and structured analysis, the core skills required in ML interviews.

You cannot perform well if your nervous system is dysregulated. Calmness is not a luxury; it is a performance variable.

 

Protect Your Identity Going Into Tomorrow

One of the deepest problems candidates face is identity anxiety:
“What if I fail?”
“What if they think I’m not good enough?”
“What if I blank out?”

These fears drain cognitive bandwidth.

Strong candidates reframe identity the night before:

“I’m not here to prove myself—I’m here to demonstrate how I think.”
“I don’t need to be perfect—I need to be structured.”
“I am not being evaluated alone—the interviewer is collaborating with me.”

This mental reframing dramatically reduces pressure and increases clarity.

Interviewers don’t expect perfection. They expect reasoning.
And reasoning thrives when identity is calm.

 

SECTION 3 - Technical Revision Checklist: What to Review the Night Before an AI/ML Interview

The night before an AI or ML interview is not the time to learn new concepts. It is the time to reactivate, organize, and stabilize the knowledge you already have. Your goal isn’t to “cram everything,” but to ensure that your thinking is accessible, structured, and ready under pressure. Weak candidates panic-review dozens of topics hoping something sticks. Strong candidates focus instead on refreshing the mental pathways that support clear reasoning.

The difference between the two approaches is huge.
One generates cognitive overload.
The other generates clarity.

This section explains exactly what ML engineers should revise the night before, what matters, what doesn’t, and how to optimize your mental framework for the next day.

 
1. Re-activate Your Internal ML Map (Not the Entire Curriculum)

Interviewers do not expect you to walk in with every algorithm memorized. But they do expect that your understanding of ML is navigable, meaning you can move cleanly from objective → data → model → metric → tradeoffs → risks.

So the night before the interview, don’t open textbooks.
Open a mental map.

Walking through your conceptual map is calm, deliberate, and stabilizing. You’re not learning, you’re reconnecting neural highways you’ve built over years.

This conceptual rehearsal can be as simple as:

  • “If the interviewer asks for a baseline, what’s my process?”
  • “If labels are noisy, how do I reason about it?”
  • “If there’s class imbalance, which levers do I pull first?”
  • “If the metric changes, how does the modeling approach shift?”

The point is not to achieve encyclopedic recall, the point is to restore mental fluidity.

A candidate who can think clearly beats a candidate who memorized everything.

 

2. Refresh Your “Tradeoff Vocabulary”

ML interviews are fundamentally tradeoff conversations.
Latency vs. accuracy.
Interpretability vs. complexity.
Freshness vs. stability.
Cost vs. performance.

If your ability to articulate tradeoffs is sharp, everything else becomes significantly easier, including system design, model justification, and debugging.

So the night before, review tradeoffs like a bilingual speaker reviewing grammar:

  • Deep models → high accuracy, low interpretability
  • Linear models → low variance, high explainability
  • Tree-based models → strong tabular performance, limited on high-dimensional signals
  • Feature engineering → boosts signal, requires domain time
  • Online inference → latency-constrained, memory-sensitive
  • Batch inference → more flexibility, slower feedback loop

Interviewers evaluate tradeoff fluency as a proxy for seniority.
Being able to speak in tradeoffs is one of the highest ROI skills you can refresh.

This is closely related to how senior ML candidates demonstrate thinking in system design rounds, described in:
➡️Mastering ML System Design: Key Concepts for Cracking Top Tech Interviews

When your tradeoff vocabulary is fluent, your reasoning becomes persuasive instead of vague.

 

3. Review the ML “Case Skeletons” You’ll Use in Answers

Every ML problem, whether churn prediction, demand forecasting, ranking, fraud detection, or text classification, follows a generalizable skeleton.

The night before the interview, rehearse 2–3 of your strongest case skeletons:

  • Classification
  • Ranking/Recommenders
  • Time series forecasting
  • Anomaly detection
  • NLP (classification or generation)

Each should include:

  • the problem setup
  • potential data issues
  • typical modeling paths
  • evaluation metrics and pitfalls
  • key tradeoffs
  • deployment considerations

This is not about memorizing answers, it is about having mental scaffolding ready so that any problem can be attached to an existing structure.

When your brain has a clear structure, it doesn’t panic.

 

4. Refresh Your LLM and Modern AI Knowledge-Lightly

You do not need to re-learn transformers the night before an interview.
But you should lightly refresh:

  • high-level transformer architecture (encoder vs decoder)
  • fine-tuning vs LoRA vs retrieval-augmented generation
  • hallucination mitigation strategies
  • prompt design intuition
  • evaluation methods for LLMs
  • latency considerations for large models

This takes 20 minutes, not three hours.

And because LLM awareness is now a default expectation in 2025–2026, you want to ensure you can speak fluidly and confidently about modern AI systems, even if the role is traditional ML.

 

5. Refresh Core Probability, Metrics, and Error Behaviors

ML interviews rarely test advanced math, but they do test:

  • bias/variance intuition
  • overfitting vs underfitting
  • ROC vs PR curves
  • precision vs recall
  • calibration
  • distribution shift
  • expected value reasoning

A clear mental grip on these concepts translates to confident diagnostics during interviews.

Again, this takes minutes, not hours.

The point is not to become a mathematician overnight.
The point is to ensure your fundamentals feel solid, so you don’t spiral when an interviewer asks you to interpret a metric.

 

6. Review Your Own Portfolio and Projects, Especially Failure Cases

One of the most embarrassing mistakes candidates make is not remembering the details of their own prior projects.

The night before:

  • revisit your best ML projects
  • rehearse the reasoning behind your choices
  • review the data challenges
  • remember failures and mitigation
  • refresh constraints you worked under
  • recall what you would do differently today

Interviewers love discussing real-world mistakes, because it signals maturity, not weakness.

A candidate who speaks candidly about project failures sounds seasoned and trustworthy.
A candidate who hides weakness sounds inexperienced.

 

7. Practice Two to Three Short Think-Aloud Sessions

This is the part most candidates skip, and it’s also the highest-return activity.

Spend 10–15 minutes thinking aloud through:

  • one modeling question
  • one system design question
  • one data ambiguity question

Speaking aloud forces:

  • structure
  • clarity
  • pacing
  • simplicity
  • tradeoff reasoning
  • emotional control

This small warm-up dramatically improves next-day performance because it primes your cognitive pathways for public reasoning.

Think of it like stretching before a race.
You don’t skip it.

 

SECTION 4 - The Environment Setup Layer: How to Engineer a Zero-Friction Online Interview Space

Even the strongest ML or AI candidate can sabotage their own interview without realizing it, not because of insufficient knowledge, but because they ignored the silent force that governs online interviews: environmental friction.

Environmental friction is any small, preventable factor that distracts, disrupts, or destabilizes your thinking. In person, companies control most of the environment. But in online interviews, you own everything, lighting, sound, internet quality, background noise, device reliability, your physical posture, and even how easily you can reach water or notes.

High-performing candidates don’t rely on luck.
They engineer an environment that supports clarity, calmness, and cognitive flow.

This section breaks down the environmental optimization strategies used by strong online interviewers, the invisible details that interviewers subconsciously notice, even when they don’t comment on it.

 

The Goal: Create a Space Where Your Brain Only Does One Job Thinking

Most candidates underestimate the mental load created by a messy environment.

Every distraction, every noise, every small technical glitch steals cognitive resources from the part of your brain that should be framing problems, thinking aloud, articulating tradeoffs, or reasoning through design constraints. Strong candidates optimize their environment so thoroughly that nothing competes with their cognition.

The goal is simple:

Reduce environmental entropy → Increase cognitive bandwidth.

Let’s break down how.

 

1. Control the Visual Frame: What Your Camera Should Communicate

Contrary to what people think, the visual setting of your call influences an interviewer’s perception of your clarity, organization, and seniority.

You don’t need an aesthetic room.
You need a clean, distraction-free frame.

Strong candidates think about three elements:

a. Background Neutrality

A plain wall is ideal.
If unavailable, remove clutter behind you.
Avoid anything visually “busy” or reflective.

This creates a sense of calm.
It also helps interviewers focus entirely on your words.

b. Lighting Placement

Good lighting isn’t cosmetic. It keeps your face visible, expressive, and readable, all important in a collaborative discussion.

Correct placements:

  • A soft light facing you (desk lamp or ring light)
  • Avoid backlighting (window behind you)
  • Avoid harsh side-light that shadows half your face

Good lighting signals professionalism and reduces visual strain for the interviewer.

c. Camera Positioning

Your camera should be at eye level, not above (intimidating angle), not below (distorted angle), not off to the side.

This simple adjustment makes your communication appear more direct and confident.

 

2. Engineer Perfect Audio: Your Voice Must Be Clearer Than Your Thoughts

Interviewers can forgive weak video quality, but poor audio kills interviews.

Why?

Because poor audio breaks cognitive flow.
It forces interviewers to work harder.
It creates subconscious frustration.
It derails nuanced explanations.
It makes you appear unprepared.

Strong candidates treat audio like a mission-critical system:

a. Use an external microphone if possible

Even a budget USB mic is miles better than a laptop mic.

b. Mute all background noise sources

Fans, windows, phone alerts, Slack notifications, everything off.

c. Test for echo

Bare rooms cause reverb.
Use curtains, rugs, or blankets if needed.

d. Run a 10-second recording of yourself

This reveals issues you didn’t know existed, like:

  • hissing
  • static
  • overly quiet volume
  • distortion
  • mic rubbing on clothing

Good audio communicates seriousness and competence. It elevates everything you say.

 

3. Build a Zero-Failure Tech Setup: Treat Your Devices Like Infrastructure

A surprising number of ML candidates lose interviews due to technical failures, internet drops, laptop overheating, browser crashes, audio switching unexpectedly, Zoom updates mid-call, etc.

Strong candidates test each component like an engineer running a pre-deployment checklist.

a. Internet Stability > Internet Speed

A stable 10 Mbps connection beats an unstable 1 Gbps line.

Test stability by:

  • Running a speed test
  • Running a ping test
  • Switching to a wired Ethernet cable if possible
  • Keeping your phone hotspot ready as backup

b. Update Everything the Day Before

Zoom, Google Meet, VS Code, Jupyter Notebook, browser extensions.

Auto-updates during interviews are catastrophic.

c. Close ALL non-essential apps

Especially resource-hungry ones:

  • Chrome tabs
  • Slack
  • Notion
  • Background sync tools
  • GPU-heavy apps

This prevents lag during coding rounds or model walkthroughs.

d. Always keep your laptop plugged in

Low battery → throttled CPU → slower execution → lag in screen share.

This is one of the most overlooked performance killers.

 

4. Optimize Your Cognitive Comfort: Small Physical Factors, Big Mental Impact

The physical environment affects your working memory more than people realize.

Strong candidates create a comfort-controlled micro-environment:

  • Chair height adjusted so arms rest comfortably
  • Water placed within reach
  • Temperature slightly cool to maintain focus
  • Phone placed face-down and out of sight
  • Desk decluttered to reduce visual noise

The fewer micro-discomforts your brain manages, the more reasoning power it has available.

 

5. Create a Quick-Access Workspace for Coding or Whiteboarding

For ML and coding interviews, your digital workspace matters.
It shapes the clarity of your communication.

Strong candidates pre-configure:

  • A clean VS Code window
  • A single notebook file
  • A pre-tested virtual environment
  • Clear font size (14–16) for readability
  • No distracting themes or plugins
  • A whiteboard tab or tool ready for quick diagrams

This eliminates interruptions during the technical flow and makes the conversation faster and smoother.

 

6. Environment Setup Reduces Interviewer Cognitive Load

Your interviewer is also human.
A frictionless environment makes them feel at ease.

When audio is clear…
When video is stable…
When your face is well lit…
When nothing glitches…
When the conversation flows smoothly…

…interviewers subconsciously attribute that smoothness to your competence.

They think:
“This candidate feels senior.”
“This candidate communicates clearly.”
“This candidate will collaborate well.”

Even though it was your environment doing half the work.

This is similar to how strong ML candidates elevate impressions during recruiter screens, discussed in:
➡️The Forgotten Round: How to Ace the Recruiter Screen in ML Interviews

Small details compound into strong signals.

 

7. The Best Environment Is One You Don’t Notice

When your environment is engineered correctly:

  • nothing distracts you
  • nothing surprises you
  • nothing breaks your flow
  • nothing steals focus
  • nothing increases anxiety

This is the ultimate goal.

A well-designed environment disappears, allowing your cognition to dominate effortlessly.

Weak candidates rely on a quiet day.
Strong candidates create one.

 

Conclusion - The Night-Before Ritual That Turns Anxiety Into Readiness

The night before an AI or ML interview isn’t about cramming; it’s about calibrating your mind and environment for performance. At this stage, you already know the material you know, and the material you don’t. Trying to force new concepts into your head only increases anxiety and cognitive overload. The real objective is to enter the interview with clarity, confidence, presence, and technical sharpness.

What most candidates underestimate is how much interview performance depends not on knowledge, but on state of mind. A calm candidate frames better, reasons better, adapts better, and communicates better. A stressed candidate misinterprets questions, jumps into irrelevant details, and spirals into over-explanations.

By creating a repeatable “night-before system,” you build psychological safety and emotional stability. Your mind knows exactly what to expect. There are no surprises. You’re not scrambling. You’re not rushing. You’re not wondering what to prepare. You’re simply walking through a well-designed ritual.

And by preparing your physical and digital environments, laptop, lighting, noise conditions, audio, code tools, network stability, you eliminate the hidden friction points that derail otherwise strong candidates. Interviews are performance moments. Performance requires controlled conditions.

Whether you're interviewing for ML engineering, AI research, data science, or applied LLM roles, the same truth holds: interview excellence is not an event; it’s a system. A routine. A mental model. A set of repeatable habits that allow you to show up as your strongest self.

Tomorrow won’t be perfect. Interviews never are. But when you’ve prepared your mind, your environment, your tools, and your fallback strategies, you give yourself the best possible chance to think clearly, reason deeply, and handle surprises with calm professionalism.

That’s how top candidates win interviews.
And now, you have the checklist to join them.

 

FAQs

 

1. Should I study new ML topics the night before?

No. Studying new material increases anxiety and offers almost no performance benefit. The night before is for reviewing frameworks, mental models, and high-level patterns, not learning new algorithms.

 

2. How much sleep should I get before an AI interview?

Seven to nine hours. Sleep has a direct impact on reasoning, working memory, and stress regulation, all core ML interview skills. Lack of sleep kills cognitive performance more than lack of studying.

 

3. Should I practice a mock interview the night before?

Only if it’s light, 15–20 minutes max. Avoid full mocks; they can cause performance dips, negative ego spirals, or unnecessary stress. Instead, do framing drills or small reasoning warm-ups.

 

4. What if I feel underprepared?

You are not supposed to feel “fully prepared.” No one ever does. Instead of focusing on gaps, focus on controlling the controllables: structure, clarity, calmness, environment, and energy.

 

5. Should I revise coding the night before?

Do a light warm-up: syntax reminders, basic operations, simple helper functions. Don’t push yourself into debugging-heavy coding the night before, it drains focus.

 

6. Should I rehearse answers to behavioral questions?

Yes, but not memorized scripts. Revisit your career stories and impact statements, especially using frameworks like STAR or one-minute narratives.

 

7. What items should I have on my desk during an online AI interview?

A short checklist:

  • Water
  • Scratch paper
  • Pen
  • Your resume (printed or digital)
  • One-page ML frameworks cheat sheet
  • Fully charged laptop + charger
  • Backup hotspot or tethering option

Minimal and functional is best.

 

8. Should I keep notes or resources open on my computer?

No. Interviewers often detect tab switching, long pauses, or off-screen reading. Your answers must come from reasoning, not searching. Keep only essential windows open.

 

9. What if my internet drops during the interview?

Have a backup plan ready beforehand:

  • mobile hotspot
  • alternative device signed into the meeting
  • phone dial-in if available

Being prepared prevents small disruptions from becoming disasters.

 

10. How do I calm nerves the night before?

Use short, proven psychological resets:

  • 5-minute box breathing
  • 10-minute walk
  • Journaling what you can control
  • Light stretching
  • Setting up your environment early

Your goal isn’t to eliminate nerves, just to reduce noise in the system.

 

11. Should I review ML formulas or derivations?

Only the ones you routinely use: gradients, common loss functions, bias-variance, regularization effects, transformer blocks, etc. Don’t force memorization; focus on conceptual clarity.

 

12. Should I rehearse system design the night before?

Yes, but lightly. Review your frameworks, not full designs. Remind yourself how to structure your thinking:

  • framing
  • constraints
  • data
  • modeling
  • tradeoffs
  • evaluation
  • monitoring

A 10-minute review is enough.

 

13. What should I wear for an online AI interview?

Simple, clean, professional. Avoid distracting colors or patterns. Your goal is to make the interviewer forget about the visuals and focus on your thinking.

 

14. Should I tell the interviewer if I'm nervous?

No, unnecessary vulnerability can shift the interviewer’s perception. What is appropriate is taking intentional pauses, breathing, and asking clarifying questions. Those behaviors signal confidence even when you're nervous.

 

15. What’s the most important thing to do the night before?

Stabilize your cognitive baseline.
Your brain should be rested, calm, and clear. The night before is about optimizing your mental readiness, not adding more knowledge.