OnPapr.ai

Most AI resume tools lie to you.
We won’t.

Paste any job. Get an honest read — strong, solid, stretch, or skip — before you spend an hour tailoring.

Built by an engineer who got tired of AI slop. Calibration before tailoring.

What the JD wantsVerdictWhat you’ve actually shipped

Distributed training at scale

Multi-GPU production pipelines

Cross-platform model deployment

Shipped inference services to prod

Top-venue first-author papers

Contributing author, peer-reviewed

Honest read

Strong fit

Strong
Solid
Stretch
Skip

Worth your shot, before you spend an hour tailoring.

Here’s what we mean

Five places
we won’t lie.

  • Tell you every job is a great fit.
    Honest pre-tailor read first. Sometimes the answer is no.
  • Sell you fifty templates, all marketed as ATS-safe.
    The open-source LaTeX templates engineers actually use — each one flagged honestly for ATS.
  • Embellish your experience to fill the page.
    Only your real work. Every line traces back to what you shipped.
  • Hand you a generic intro script for the call.
    Bullet talking points tied to your profile and this JD.
  • Pile on LeetCode questions and call it prep.
    Drill the right ones with a Socratic interviewer.

What you get

The whole loop,
not just a resume.

01

Calibrate.

Before you spend an hour tailoring, see if the job is even worth your shot. Four-band read, honest about the misses.

Strong
Solid
Stretch
Skip

Honest read

Strong fit · 82

02

Tailor.

Resume and cover letter authored from what you've actually shipped. Real LaTeX, sharper specifics. No embellishment.

Profile says

Worked on backend systems and ML infrastructure at a previous employer.

Tailored to this JD

What lands on the page

Built a production training pipeline; scaled to large datasets with distributed multi-GPU training; deployed inference via cross-platform runtimes in production.

Real LaTeX · never embellished · every line traces to your profile

03

Rehearse.

Open the lead into a Socratic interviewer. Coding, system design, behavioural. Never the verbatim problem.

Interviewer
Walk me through how you'd design distributed training across an unstable network — what fails first?
You
Gradient all-reduce. I'd split the optimizer state across ranks and overlap comm with backward to mask straggler tail.
Interviewer
Good. What's your fallback when a rank drops mid-step — checkpoint reload or local skip?

Socratic interviewer · coding, system design, behavioural

Free to try.
$5 a day, $19.99 a month.

Calibration and phone-screen prep are on every tier. Free covers three generations a month and a preview of the interview prep. Day pass or Pro unlocks unlimited tailoring and full rehearsal.