Most engineering resumes read like a ticket queue. Launch CV rewrites them with the language hiring managers actually scan for — latency, throughput, scope, ownership, baselines — and keeps formatting ATS-clean for Workday, Greenhouse, Lever, and 12 more.
Pre-loaded keyword library
+ 200 more across Cloud, DevOps, Security, ML, and mobile. AI picks the right subset per JD.
The diff · 3 real rewrites
Before
$ fixed slow db queries
After · AI rewrite
Cut p95 read latency from 240ms → 48ms on the payments service via index rewrites and connection pooling — saved an estimated $34k/yr in DB IOPS.
Before
$ shipped some features
After · AI rewrite
Owned end-to-end delivery of 7 features across Q3/Q4, growing weekly active dev seats by 28% (3.2k → 4.1k) on the IDE plugin.
Before
$ did some on-call work
After · AI rewrite
Led on-call rotation for tier-0 services (12-engineer roster, 24×7), cutting MTTR from 41 min → 9 min via runbook and alert hygiene.
Engineering-specific fixes
Problem
Bullets sound like JIRA tickets
We fix it with
AI rewrites ‘fixed bug in payments’ as ‘reduced p99 checkout latency by 38% across 3 regions’.
Problem
Open-source isn't on the resume
We fix it with
Dedicated Projects section with stars, commits, and contribution role auto-detected from GitHub.
Problem
ATS rejects clever layouts
We fix it with
Engineering-tested templates: no tables, no graphics, no LaTeX-looking sidebars.
Problem
Senior signals don't pop
We fix it with
AI surfaces scope: team size, system tier, on-call rotation, RFC ownership.
Engineering sections covered
Cloud, languages, frameworks, datastores — grouped, deduped, ATS-parseable.
Repo name, stars, commits, role. Maintainer or contributor — auto-detected from GitHub if linked.
RFCs, architecture migrations, scale milestones (10× users, 100× requests, $X cost saved).
Rotation size, MTTR, incident leadership, postmortem ownership.
AWS, GCP, K8s, security — with issue date and credential ID.
Conference talks, blog posts, podcast appearances, internal RFCs cited externally.