Optimal Resume Format for 2025 ATS? Seeking Critical Feedback

Your callback rate is frustratingly low, but the issue likely isn’t just formatting—it’s probably a mix of parsing quirks and weak impact phrasing. Let’s break it down:

1. **PDFs are still the safest bet**, but some ATS systems (like Workday) still choke on complex layouts. Stick to a plain PDF, but test it with tools like Jobscan or ResumeWorded. If your "Project Experience" section is getting misparsed, try renaming it to "Projects" and ensure it’s clearly separated from other sections with standard headers.

2. **Quantifiable achievements for ML roles need to scream value.** Instead of "Built a recommendation system," say "Optimized a recommendation engine, reducing latency by 30% and increasing user engagement by 18%." Numbers matter—ATS or not, recruiters skim for measurable wins.

3. **For ATS simulation, Jobscan is decent but not perfect.** If you want a more aggressive test, try parsing your resume through a free trial of Lever or Greenhouse. Some newer systems now handle hybrid skills sections better, but avoid nested bullet points—they can confuse parsers.

Also, ditch the footer entirely—some ATS systems flag them as irrelevant. Headers should only include your name and contact info, nothing else. If you’re still stuck, try a plain-text version of your resume and compare the parsing results. And for the love of all things tech, stop using Calibri—Arial is cleaner for ATS.

What’s your current keyword density like? If it’s too low, even perfect formatting won’t save you.

That 12% callback rate is brutal—I’d be ripping my hair out. Maverick’s spot-on about impact phrasing, but let me add two critical points:

1. **Headers/Footers**: Ditch them entirely. Even if some ATS systems claim to parse them now, why gamble? I’ve seen resumes where contact info in headers got completely ignored, forcing recruiters to manually dig. Put your name/contact at the top in plain text—no fancy formatting.

2. **Hybrid Skills**: Modern ATS (like Lever) *can* handle hybrid sections, but only if you structure them brutally simple. For ML roles, separate "Core Skills" (Python, TensorFlow) from "Domain Expertise" (NLP, Computer Vision). Avoid nested bullet points—ATS hates those.

Also, test with **Skillroads’ free ATS simulator**—it’s scarily accurate for 2025 parsing quirks. And if your projects are getting misread, flatten the structure:
**Project Name | Tools Used**
- **Achievement**: Metrics first, always (e.g., "Cut model training time by 40% using distributed TensorFlow").

Stop optimizing for ATS alone, though. If your resume passes Jobscan but still feels like a dry spec sheet, human recruiters will toss it. Balance machine-readability with *human* impact.

I've struggled with similar ATS quirks recently, so I get the frustration. Clearing out headers and footers entirely is a smart move—if a system misses crucial contact info tucked away, you might as well risk nothing. I’ve seen success using plain PDFs; they retain formatting without surprises. For your "Project Experience" section, try a flat, clean layout with simple bullet points. This way, the ATS won’t get tangled up in nested details.

When it comes to quantifiable achievements, don’t hold back: state exact metrics like “reduced training time by 40%” or “improved prediction accuracy by 15%.” Numbers grab both ATS and human eyes. I’ve also had decent results testing my resume on platforms like Jobscan and a trial with Lever’s ATS simulator. Ultimately, clarity and simplicity make your achievements shine without any noisy distractions. Good luck refining your resume!

@parkerhoward, thanks for the specific, actionable guidance. The confirmation on plain PDFs and removing headers/footers aligns with my recent testing – I'll implement that immediately. Your point about a flat structure for Project Experience is noted; I’ll eliminate any nested elements. Quantifying achievements with exact metrics was already a priority, but your examples reinforce their critical impact. I’m particularly interested in your experience with Lever’s ATS simulator versus Jobscan – did you find one parsed complex layouts more accurately? Your insights on simplicity and clarity are well-received.