Job Search Toolkit
Resume templates, real application funnel data and portfolio projects that get you hired.
An AI job search is a numbers game with a conversion problem: the data here shows only about 25% of resumes pass ATS screening. This toolkit attacks both ends — machine learning resume templates with section-by-section do's and don'ts plus ATS keyword guidance, and real application-funnel data tracking applications, responses, screens, onsites and offers for junior and mid-level ML and LLM engineer searches.
The strategy section compares referrals, direct outreach and cold applications by response rate, with common rejection reasons and their fixes, and timeline expectations per stage. The portfolio tab specs four projects — difficulty, time estimate, skills demonstrated and core functionality — because a well-chosen build moves a machine learning resume further than another certificate.
Explore: How AI screens resumes · Build an ML portfolio · Remote ML jobs
Applicant Tracking System - software that screens resumes before humans see them
- •Use standard section headers (Experience, Education, Skills)
- •Avoid tables, text boxes, headers/footers
- •Use standard fonts (Arial, Calibri, Times New Roman)
- •Save as .docx or PDF (not .pages or .odt)
- •Include keywords from job description naturally
- •Use full acronyms once (Natural Language Processing (NLP))
- •Avoid graphics, logos, photos (except header)
- •Use bullet points, not paragraphs
Keyword matching: ATS scans for keywords from job description
Resume Templates
Career Switcher to ML Engineer
Machine Learning Engineer
Backend Engineer transitioning to ML
- ✓Lead with ML projects and skills
- ✓Quantify everything (numbers, percentages, scale)
- ✓Show GitHub and portfolio prominently
- ✓Tailor resume to each job (use their keywords)
- ✓Keep it 1 page (2 pages max if 10+ years experience)
- ✓Use clean, ATS-friendly formatting
- ✓Proofread for typos (auto-reject if sloppy)
- ✗Don't lead with old non-ML work
- ✗Don't list every technology you touched once
- ✗Don't use fancy formatting (ATS can't parse it)
- ✗Don't lie about skills (you'll be tested)
- ✗Don't include irrelevant experience (waiter job 10 years ago)
- ✗Don't use generic summaries ('hardworking team player')
- ✗Don't forget to update LinkedIn to match
New Grad ML Engineer
Junior ML Engineer / ML Engineer I
Recent CS grad or bootcamp grad
Senior ML Engineer
Senior ML Engineer / Staff ML Engineer
5-10 years ML experience
Common Resume Mistakes
No GitHub link or projects
ML is hands-on - no projects = no credibility
Fix: Add GitHub prominently, showcase 3-5 projects
Listing skills you barely know
You'll be tested in interview and fail
Fix: Only list skills you can discuss in depth
Generic, non-quantified bullets
Can't assess impact or level
Fix: Add numbers: X% improvement, Y users, Z scale
2+ page resume for junior role
Recruiters spend 6 seconds per resume
Fix: Keep it 1 page, be ruthless about what's relevant
Fancy formatting that breaks ATS
Never reaches human reviewer
Fix: Use simple, clean formatting
FAQ
Common questions, answered with the data on this page.
How do I get a machine learning resume past ATS screening?
Only about 25% of resumes pass ATS, per the data on this page — the ATS callout explains how keyword matching works and lists concrete tips. The three templates above are structured for it, each with role-specific do's and don'ts. For the screening side in depth, read how AI screens resumes.
How many applications does an AI job search take?
The funnel cards above show real averages by role and level — applications, responses, phone screens, onsites and offers for junior and mid-level ML engineer and mid-level LLM engineer searches, with conversion rates at each step and roughly how many days the full loop takes. Use them to set a weekly application target.
Which application strategy has the best response rate?
The strategy cards compare referrals, direct outreach and cold applications head-to-head with response rates — referrals consistently convert best, while cold applications need the most volume. Each card lists how-to steps, and the rejection-reasons section shows the most common fixes when a channel underperforms.
What portfolio projects help land a machine learning job?
Projects that demonstrate job-relevant skills end-to-end. The four specs on this page include difficulty, time estimates, skills demonstrated and core functionality, plus selection tips and common mistakes — a focused build beats a tutorial clone. Our machine learning portfolio guide covers how to present them.