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Feature -- Resume AI

A resume that reads like an engineer wrote it.

Resume AI rewrites your resume the way frontier AI labs actually read them -- calibrated against thousands of senior offers in the OpenTalent network. Reads your real work, not your buzzwords. Tailors per match. Edits the way a senior engineer would.

Try Resume AIHow it works
FREE for network members|PER-MATCH tailoring|~2 MIN per draft
// resume.diff -- tailored for anthropic -- post-training
DIFF
FINAL
JSON

// BEFORE

// EXPERIENCE

Worked on ML projects using PyTorch and TensorFlow.

Built scalable systems handling millions of requests.

Strong communication and problem-solving skills.

// SKILLS

Python, ML, AI, Deep Learning, NLP, Computer Vision, Cloud, Agile

// AFTER

// EXPERIENCE

Trained a 7B reward model on 220K preference pairs; +12% on internal IFEval, shipped to prod in 6 weeks.

Built and owned the eval harness behind 4 RLHF model releases; reduced regression detection from days to 90 mins.

Wrote the 3-page proposal that consolidated reward design across 2 teams; cited in mid-year planning.

// FOCUS

Post-training -- RLHF -- reward modeling -- evals. PyTorch, FSDP, vLLM.

Tailored to Anthropic -- post-training12 EDITS -- CONFIDENCE 96%

Calibrated against senior offers at

AnthropicOpenAIDeepMindMistralCoherePerplexityxAISarvamRekaRunwayScale AIFractal

// The problem

Most AI engineer resumes are bad in the same five ways.

We've reviewed thousands. The same mistakes show up over and over -- the kind that get an engineer screened out by a recruiter before a senior practitioner ever reads the document.

What most resumes doSCREENED OUT

  • Long skills lists with no proof attached -- Python, ML, AI, NLP, CV, Cloud.
  • "Built scalable systems" and other phrases the reader cannot picture.
  • Buried the actual frontier work three bullets deep, under generic SaaS work.
  • The same document sent to a pre-training role and an applied AI role.
  • No numbers. No specifics. No way to tell if the person shipped or just attended.

What Resume AI producesSHORTLISTED

  • Replaces skill soup with the four or five tools you actually used in shipped work.
  • Rewrites every bullet to lead with the verb and end with a measurable result.
  • Surfaces the work that matches the role -- even if it's buried in side projects.
  • Generates a tailored version per match, with the right framing for the lab.
  • Tells you which bullets are weak and what evidence from your repos to add.

// How it works

From rough draft to lab-ready in four steps.

No intake form, no template. Drop in what you have and Resume AI does the work.

01

Drop in any draft

PDF, docx, LaTeX, plaintext -- even a LinkedIn export. Resume AI parses what you have and asks for nothing more than what's already there.

02

Read your real work

Connect GitHub, Scholar, and any project write-ups. The model maps your shipped work onto the bullets you claimed -- and pulls in what you forgot.

03

Rewrite for the bar

Every bullet is rewritten to senior-IC standard: leads with the verb, names the technique, ends with a measurable result. No buzzwords, no filler.

04

Tailor per match

One canonical resume, infinite tailored versions. Each AI Job Match generates a draft tuned to the role's keywords, lab voice, and grading rubric.

// Tailored per lab

The same engineer. Three different resumes.

A pre-training resume is not an applied-AI resume. Resume AI knows the difference -- and rewrites accordingly.

Anthropic -- Sr. Post-training Research Engineer

Resume AI emphasizes the alignment-flavored work -- reward modeling, RLHF, eval design -- and reframes generic ML bullets to lead with the same language the team uses internally.

  • Bullets reweighted toward reward modeling, IFEval, preference data curation.
  • Tone tuned for senior-IC clarity -- short, precise, evidence-led.
  • Skills section drops generic ML tags; surfaces FSDP, vLLM, TRL, and DPO.
  • Cover note auto-drafted, citing the team's recent published work.

// Sample tailored bullet

"7B reward model on 220K preference pairs; +12% on internal IFEval, shipped to production in 6 weeks.

"Owned the eval harness behind 4 RLHF model releases; reduced regression detection from days to 90 minutes.

"Wrote the 3-page proposal that consolidated reward design across 2 teams; cited in mid-year planning.

OpenAI -- Staff Agent Engineer

Resume AI surfaces the planner, tool-use, and production agent work -- and de-emphasizes pre-training detail that's less load-bearing for this team.

  • Bullets reweighted toward planning, function-calling, and agent eval.
  • Stack callout surfaces orchestration tools: ReAct, LangGraph, custom planners.
  • Project section pulls in your tool-use side projects from GitHub.
  • Cover note auto-drafted around production agent shipping experience.

// Sample tailored bullet

"Shipped a multi-step tool-use agent serving 4K daily users; reduced hallucinated tool calls by 73% via constrained decoding.

"Designed the agent eval rubric adopted by the platform team; covers planning, tool selection, and recovery.

"Built the environment wrapper that turned 12 internal APIs into agent tools with typed schemas and replay support.

DeepMind -- Pre-training Research Engineer III

Resume AI emphasizes scaling work, distributed training experience, and any published research -- and reframes ambiguous bullets with concrete model and data scales.

  • Bullets reweighted toward parallelism, scaling, infra-research interface.
  • Numbers added wherever scale, GPU hours, or model size is provable from your repos.
  • Publications moved up; informal write-ups treated as preprints when load-bearing.
  • Cover note auto-drafted referencing a specific research direction the team has shipped on.

// Sample tailored bullet

"Led pre-training of a 13B dense model over 2.4T tokens on 1,024 H100s; +8% MMLU vs. internal baseline at fixed compute.

"Implemented 3D parallelism (DP x TP x PP) on top of FSDP; cut step time 24% at our scale.

"Co-authored a workshop paper on data-mix curation at frontier scale; cited 40+ times.

Sarvam -- Sr. Applied AI Engineer

Resume AI surfaces production AI work, retrieval and eval design, and India-context experience -- and reframes research bullets in terms of product impact when relevant.

  • Bullets reweighted toward RAG, latency, evals, and prod observability.
  • India/Indic context surfaced where present; localized model evals foregrounded.
  • Latency and cost numbers added where retrievable from project notes.
  • Cover note auto-drafted around scale, reliability, and Indic-language reach.

// Sample tailored bullet

"Built a hybrid RAG pipeline for 11 Indic languages; -42% p95 latency and +18% retrieval-grounded answer rate vs. dense-only baseline.

"Designed the eval harness for Indic instruction-following used in two model releases.

"Shipped cost & latency observability for the AI gateway serving 1.2M requests/day; reduced cost per query 31% in one quarter.

// What it catches

Six things Resume AI fixes on the first pass.

Most of these take a senior engineer two hours to catch. Resume AI catches all six before you submit.

Kill the keyword soup

Skill lists with 30+ tags read as low-confidence. Resume AI keeps only the tools you actually used in your top bullets.

Python, ML, AI, NLP, CV, Cloud, Agile, REST, SQL, MongoDB, Docker...PyTorch -- FSDP -- vLLM -- TRL -- DPO

Verb-first, result-last

Rewrites every bullet to lead with what you did and end with a measurable outcome. Drops the soft openers.

Worked on building scalable systems for ML inference.Cut p95 inference latency 38% on a 7B model via paged attention; serving 2M req/day.

Add specificity

Pulls scale, time, and result numbers from your repos and notes -- and asks you for the ones it can't find.

Improved model performance significantly.+12% on IFEval; -2.1x hallucination rate on internal benchmark vs. prior release.

Surface buried frontier work

Side projects and weekend repos often contain the most differentiated work. Resume AI promotes the right ones.

Side project: chatbot using LLMs.Open-source agent framework with structured-output decoding; 2.4K GitHub stars; cited in 3 lab tooling repos.

Tone-match the lab

Anthropic reads differently than xAI. Resume AI tunes voice and emphasis to the lab -- without losing your own voice.

Built innovative AI solutions leveraging cutting-edge ML.Designed the eval rubric that caught 9 of 12 regressions in pre-release sweeps.

Flag weak bullets

Each bullet gets a confidence score. Low-confidence ones are flagged with a prompt -- "what shipped, what was the result?"

Collaborated with cross-functional teams on AI initiatives. (flagged: low signal)Promote or drop? Suggested replacement drafted from your repo activity.

// By the numbers

It works. Here's the data.

Tracked across every tailored resume and matched panel in the OpenTalent network.

11K+

Resumes tailored through Resume AI this quarter.

// ACTIVE USAGE

2.3x

Higher screening pass rate vs. canonical resume in matched panels.

// EFFECT

8 min

Median time from drop-in to tailored, lab-ready first draft.

// SPEED

96%

Of users keep the rewritten bullets without changes after one pass.

// ACCEPTANCE

// Privacy & control

Your resume is your data. Period.

We don't train shared models on your resume. We don't pass it to labs without your explicit per-match consent. We delete it on close.

Stays in your accountPRIVATE

Your canonical resume and all tailored versions live in your private account. No lab, no recruiter, and no other network member can read them.

Per-match consentYOU APPROVE

A tailored resume is only sent to a lab after you click 'Open to introductions' on a specific match. No auto-forwarding, no shared library.

No shared trainingYOUR DATA

We do not use your resume content to train shared models that other users see. Personalization runs in your account, with embeddings scoped to you.

Owned by youEXPORT / DELETE

Export your resume and history as PDF, docx, JSON, or LaTeX any time. Close your account and everything is deleted within 30 days. Full retention rules in the Privacy Policy.

“
I'd been rewriting the same resume for three years and getting nowhere. Resume AI rewrote it in eight minutes, kept my voice, and pulled in two side projects I'd forgotten to mention. Two of the three labs that ghosted me before replied within a week.

Senior applied AI engineer -- now at a frontier lab

// FAQ

Things engineers ask first.

Does Resume AI just stuff keywords into my resume?+

No. That's exactly what we don't do. Resume AI rewrites bullets to lead with the verb, name the technique, and end with a measurable result. The model is calibrated against network-verified senior offers, not ATS keyword scrapers.

If a bullet doesn't have evidence in your repos or write-ups to back it up, Resume AI flags it instead of inflating it.

Will it write things I didn't actually do?+

No. Every claim is anchored to something Resume AI can see in your connected work -- commits, papers, project notes -- or in the existing draft you provided. When the model can't ground a claim, it asks you for a number or a piece of evidence instead of inventing one.

How tailored is "per match"? Is it just a few keyword swaps?+

It's a real rewrite. Tailoring changes which bullets are surfaced, the framing of each bullet, the stack callouts, the cover-note voice, and the project section ordering. Two tailored versions of the same canonical resume can differ in 60-70% of their lines.

Can I edit the rewritten bullets?+

Yes. Resume AI is a draft tool, not a black box. Every rewritten bullet has an inline editor and a "regenerate with stronger evidence" button. You always own the final draft.

What formats can I export?+

PDF (with a clean, ATS-readable template), docx, LaTeX, JSON, and plain markdown. You can also drop in your own LaTeX template and have Resume AI fill it.

Is it really free?+

Free for OpenTalent network members. To join the network, apply through the five-stage screening.

// Other features

Resume AI works best with the rest of the toolkit.

Three features that compose with Resume AI. Together they're how engineers in the network actually move through the job market.

// Feature

AI Job Match

Reads your work, watches the labs, surfaces three to five real matches a week. Stealth-by-default. Resume AI tailors a version for each match it surfaces.

// Feature

Insider Referrals

Ask any engineer in the network at a target lab for a warm intro, anonymized until both sides opt in. Your tailored resume goes with the intro.

// Feature

Application Autofill

One tap to apply to a matched role with your tailored resume, cover note, and the right answers to lab-specific application forms.

The resume that sounds like the engineer you actually are.

Resume AI is free for OpenTalent network members. Drop in your draft. Get the tailored version back in eight minutes.

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