v2.6 now available

For Northeastern co-op students

Find the co-ops that actually fit your resume.

NUWorks Grader reads every posting the way you would — then scores it against your skills, flags the ones you're not eligible for, and saves the strong matches. Optional Semantic AI matches by meaning, too. All in your browser, all private.

Add to ChromeIt's free See how it works

100% Local & private

250+ Skills recognized

On-device Semantic AI (opt-in)

Free And open source

See your next co-op more clearly.Play the walkthrough

Where our users land offers

A clearer view of every opportunity

Match scores and eligibility badges, right where you browse. Spot the roles that fit before opening another posting.

NUWorks Extension Interface Discovery Page with Color-Coded Badges

NUWorks Extension Interface Search Page with Color-Coded Badges

Select any screenshot for a closer look.

A redesigned Job Explorer and popup

Match scores, an explainable breakdown for every job (now including a Semantic AI bar), smart filters, and a calm interface that follows your system light or dark theme.

New

A job's match breakdown with sub-scores and matched skills

Why use this extension?

Stop wasting time on jobs that don't match your profile. Let smart, on-device analysis do the heavy lifting.

Auto-grading & visual badges

Instantly see match scores, external-application warnings, and eligibility status with color-coded badges on the job list.

Smart filters & batch actions

Filter by match score or freshness. Save all matched jobs in one click, or clean up your saved list just as fast.

Resume parsing

Upload your PDF resume. We extract the text and skills locally to find your best matches.

Semantic AI matching Opt-in

A 33M-parameter BGE model fine-tuned for resume–job fit runs on your device and scores each posting by meaning. Match becomes 65% keyword + 35% AI, with the split shown on every card.

Implied skills

Listing React implies JavaScript; PyTorch implies Python. Related skills earn partial credit and show up as dotted chips, so you're not penalized for not spelling everything out.

Faster Job Explorer

Scores render from a single listing call — no per-job fetching. Extra details load in the background with a status chip, and the AI refinement never blocks the page.

Expanded BGE model · Tested on 24 unseen resumes · Opt-in

Semantic AI trained for resume-to-job fit

Turn on Semantic AI and a compact model fine-tuned on labeled resume–job pairs runs entirely on your device, re-reading every posting to score how well it fits by meaning—not just keyword overlap.

The Job Explorer with Semantic AI enabled, showing a blended match score plus a Keyword · AI split on every card

Fresh unseen-resume evaluation · 960 pairs

The gain holds on resumes it never saw

We froze 24 previously unused resumes across 24 career categories, paired each with 40 randomly selected NUWorks jobs, and labeled the resulting matches only after training was complete. The new int8 model improved every individual resume over the previous model.

Spearman correlation 0.768 from 0.567 with the previous model 36% higher rank correlation

NDCG@10 0.868 from 0.774 with the previous model 24 of 24 resumes improved

Int8 ONNX, semantic-only ranking. Median per-resume Spearman was 0.796, and 23 of 24 resumes scored at least 0.50. These resumes were excluded from every training, validation, and prior test split; the jobs came from the existing NUWorks pool. Metrics measure ranking quality, not hiring success.

Keyword layer

The classic scorer checks required skills, keywords, and eligibility — fast, transparent, and always on. It now also credits implied skills: React on your resume counts toward a JavaScript requirement.

Semantic layer

mdbr-leaf-mt-resume-grader is now a 33.36M-parameter BGE-small fine-tune adapted on 6,804 NUWorks resume–job labels, then replayed against a 26,677-example weighted external schedule to preserve broader matching quality. It embeds your resume and each posting, then measures how close they sit in meaning. “Built REST services in Flask” counts toward “backend web development”.

Blended score

Your final match is 65% keyword + 35% semantic. Every card shows the split (Keyword 96 · AI 79) and the details view adds a dedicated Semantic (AI) bar so you can see exactly what moved the number.

Explainable, down to the bar

Open any posting and the match breakdown shows Skills, Keywords, and Semantic (AI) side by side. Hover a card's split to see the raw cosine similarity behind the AI number.

Private by design. Your resume and every job description are embedded in a Web Worker in your browser. Nothing is uploaded.
Lightweight until you opt in. The extension ships without the model. Ticking “Semantic AI” downloads the runtime and model once (~57 MB) and keeps them cached.
Scores never wait on it. Keyword scores appear instantly; the AI refinement streams in behind a “Refining scores” chip and never blocks the page.
Honest about hardware. Before enabling, you're told it's compute-intensive on MacBook Airs, Chromebooks and older laptops. Thread count auto-scales to your CPU.