Resume, coding, system design, behavioral, fundamentals — all in one place, personalized to your target role, your interview date, and your actual gaps.
No credit card · Credits never expire
Calibrated to the interview bar at
How it works
Target role, target company, and interview date. Upload your resume.
Coding, system design, behavioral, resume rewriting, and fundamentals. All AI-graded in one place.
Get a week-by-week prep plan built around your timeline, resume gaps, and practice scores. Know exactly what to work on — and in what order — before interview day.
JD-targeted, not generic career advice
Upload your resume and paste the job description. Get a gap analysis showing what signals are missing — then rewrite weak bullets directly. The AI rewrites them targeted to that specific JD, not just "improved."
Score
Strengths
Gaps
Suggested rewrite
Built ML models for recommendation
Designed and deployed a two-tower recommendation model serving 2M daily users, reducing latency by 40%
Probability · Statistics · ML Theory · Model Evaluation
Flashcard-style quizzes across every DS/ML topic. AI grades your written answer and explains the concept gap. Your weak topics feed directly into your personalized focus.
Question
What is the bias-variance tradeoff?
Score
Answer quality
Key concept
Bias measures how far predictions are from truth on average. Variance measures sensitivity to training data fluctuations. High bias → underfitting. High variance → overfitting. Regularization, dropout, and ensemble methods manage the tradeoff.
Improvement
!Mention specific techniques: L1/L2 regularisation, dropout, early stopping
SQL · Python · ML Theory · Statistics
Not just right or wrong. A 0–100 score across correctness, time complexity, and space complexity — with specific issues flagged and explained. Plus a full model solution and a concept lesson so you understand the pattern, not just the answer.
Problem
Find the top 3 highest-paid employees in each department.
Score
Breakdown
Issues found
Highest signal at senior levels
Write a free-text architecture answer. Get AI feedback across scalability, trade-offs, data modeling, and communication clarity — every dimension that separates a 40 from an 80 at Meta.
Your answer
I would use a two-tower model for candidate generation, followed by a ranking model. Features would be stored in Redis for low-latency retrieval. The candidate pool would be refreshed hourly via batch jobs...
Score
Rubric
Issues found
Amazon LPs · Google Googleyness · Meta Values
Practice behavioral questions structured around the frameworks top companies actually use. Get AI feedback on your answer structure, specificity, and impact framing. Know which stories are landing before the real thing.
Behavioral · Influence & Leadership
Tell me about a time you had to influence without authority.
Your answer
At my previous role, I noticed our data pipeline had latency issues affecting the product team's dashboards. I didn't own that system, but I put together a short analysis showing the impact...
— answer continuesSTAR structure feedback
Strengthen your Result — quantify the impact with a metric.
Composite readiness score
Not just a grade — a roadmap. HonePrep combines your timeline, resume gaps, and practice performance into a week-by-week prep plan that tells you what to work on, in what order, and why.
Get your roadmap — freeYour personalized roadmap
Resume rewrites + Fundamentals gaps
System Design + Behavioral stories
Coding drills + full mock run-through
You're ready.
The only prep platform built for DS/ML — end to end
Pricing
Start with 100 free credits on signup. Top up when you need more.
Starter top-up
Save $3
Save $12
No subscriptions · Credits never expire · Powered by Stripe
400+ candidates · 3,200+ practice sessions · avg. score gain: +28 pts
What candidates are saying
I'd been grinding LeetCode for months and still bombing system design rounds. HonePrep's rubric feedback showed me exactly what interviewers look for. Got an offer at Stripe 3 weeks later.
Farid K.
Staff ML Engineer
The resume scoring against the actual JD was eye-opening. I had zero SQL work on my resume and was applying for a data science role. Rewrote three bullets and made it past the screen.
James T.
Data Scientist · Airbnb
My readiness score was 44 when I started. I focused on what the breakdown told me to fix. Three weeks later I was at 73 and got my first senior MLE offer. Nothing else gave me this kind of direction.
Priya M.
Senior ML Engineer · Google
Coding on LeetCode. System design on YouTube. Behavioral on your own. Resume in Google Docs. HonePrep brings it all together — personalized to your resume, your target role, and your timeline.
Start your prep — 100 credits freeTrusted by candidates targeting Google · Meta · Stripe · Airbnb