Example output. Your real review uses your own resume and the JD you paste.
ATS BY THE NUMBERS
Lever
the ATS Anthropic uses for engineering, research, and operations hiring
Writing
the differentiator the Anthropic recruiter screen weights most heavily
30 sec
what the widget above takes to score your resume against the JD
Anthropic runs every application through Lever, which scores resumes against the JD for keyword match, format, and section structure before any human sees them. The bot reads top-to-bottom, maps your text into structured fields, and scores. The way to "beat" it is to be one of the resumes the recruiter's filter surfaces, which means matching the JD keywords precisely and being parseable as structured data. The widget above runs that scoring on your resume against any Anthropic JD, in 30 seconds.
Writing quality is the screen
Anthropic recruiters explicitly weight resume bullet clarity. Vague marketing-toned bullets lose to concrete scoped ones.
ML / LLM vocabulary signal
If applying to research or ML engineering: named frameworks (PyTorch, JAX, vLLM, transformers), named training paradigms (RLHF, constitutional AI, sparse autoencoders), and named eval benchmarks.
Safety-aligned thinking
For research roles, Anthropic looks for evidence you think about safety as a first-class concern: red-teaming, interpretability, robustness, evals. Surface any of these explicitly.
End-to-end ownership signal
Anthropic engineering JDs weight projects where you owned design, implementation, and operation. "Shipped X" beats "contributed to X."
Lever-safe formatting
Lever parses two-column reliably and tolerates decorative fonts. Single-column is still safer if you also apply to firms on Workday.
Open-source weight
Anthropic recruiters notice public AI/ML contributions (a paper, an evals harness, a vLLM optimization, a popular HuggingFace model). Surface in the header.
No "passionate about AI" language
Anthropic recruiters skim past "I am passionate about AI safety" / "drawn to mission-driven companies." Replace with one specific thing you have built or read.
1. Upload your resume
DOCX or text-selectable PDF only. Image-based PDFs cannot be read by any ATS. 10MB max.
2. Paste the job description
Full JD text or the URL of the posting. The score is tailored to that exact JD.
3. Apply the rewrites
Critical and Notable edits are grouped by severity. Each shows the original, the rewrite, and which keyword or formatting rule it fixes.
4. Download the new PDF
The preview rebuilds your resume live as you accept edits. Single-column, Workday-safe, ready to submit.
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Every Monday: the specific ATS keywords showing up in newly posted JDs at Goldman, McKinsey, Google, and 20 other firms. Free, no spam.
Lever for engineering, research, and operations hiring. The same Lever instance is used across all teams.
No. Lever parses two-column reliably and tolerates decorative fonts. The recruiter screen is the binding constraint at Anthropic, not the parser.
Named ML/LLM frameworks (PyTorch, JAX, transformers, vLLM), named training paradigms, named eval methodologies, and evidence of safety-aligned thinking (red-teaming, interpretability, robustness).
Not strictly. Strong open-source ML/eval contributions (a popular HuggingFace model, a well-known evals harness, vLLM optimizations) substitute for papers in many recent hires.
~4 to 8 weeks from application to offer. The take-home (technical screen for engineering, paper-discussion for research) is the highest-leverage round.
Yes, and they matter. A referral moves you to a recruiter-read tier within ~5 days. Use Offerloop's Find feature to identify Anthropic employees from your university.
No catch. Upload your resume, paste the Anthropic JD, get the score and rewrites without an account.
Similar bar on technical depth. Anthropic weights safety-aligned thinking more heavily; OpenAI weights distribution and product surface. Tune the resume accordingly.