ATS checker · ML Engineer
Most ATS guidance is generic, recruiters in ai / ml engineer screen for specific terminology, operational language, and scope signals. Here's what actually matters.
No credit card required · Recruiter intelligence + ATS analysis
ATS terminology
Hand-curated by role. These aren't generic keywords, they're the language recruiters and ATS systems weight most for this specific role.
Recruiters and ATS systems screen for these specific terms. Missing them quietly removes candidates from consideration.
Strong action verbs that signal ownership and outcome. Generic language reads as junior or inflated.
Searchable skills
These are the named tools, frameworks, and concepts recruiters search for explicitly. Missing the relevant ones quietly removes you from consideration.
Only list what you've actually shipped or used. ATS systems reward keyword alignment, but recruiters discount unsupported claims , cross-reference each skill in at least one bullet.
Common mistakes
Notebook-only experience presented as production work
Generic 'used LLMs' without RAG, fine-tuning, or eval specifics
Before / after
Each rewrite shows the recruiter signals added and the approximate ATS lift.
Before
Built ML models using PyTorch. Deployed to AWS.
After
Trained and deployed a fine-tuned Llama-3-8B model on AWS SageMaker for ticket classification. P99 latency 280ms, $0.003 per request, +14 points F1 vs the base model on internal eval set of 12K tickets.
Why this is stronger
Replaces generic claim with full production lineage, model choice, latency, cost, and evaluated lift.
Recruiter signals added
Get an ATS score against role-specific terminology, formatting risk detection, and a recruiter-readability breakdown, free.
Free plan available · No credit card required
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