RESUME MISTAKES
Stop Losing Interviews: Fix Your ML Engineer Resume Today
Identify critical resume pitfalls and apply proven fixes to get past ATS and impress hiring managers.
How This Page Helps
This guide helps Machine Learning Engineers spot and correct resume mistakes that reduce interview chances, ensuring ATS compatibility and clear communication of technical expertise.
Identify highâimpact errors
Learn ATSâfriendly formatting
See beforeâandâafter resume snippets
Apply quick fixes with our miniâworkshop
Boost your interview rate
Common Mistakes That Kill Your Chances
Each mistake includes why it hurts, how to fix it, and before/after examples
Overly Generic Objective StatementMEDIUM
Why it hurts
- Doesn't convey specific ML expertise
- Lacks keywords that ATS look for
- Fails to capture hiring manager attention
How to fix
- Replace objective with a 2âsentence professional summary
- Highlight core ML domains (e.g., computer vision, NLP)
- Insert 3â5 relevant keywords from target job
â Before
Objective: Seeking a challenging position in a reputable company where I can apply my skills.
â After
Summary: Machine Learning Engineer with 4âŻyears experience building productionâgrade deepâlearning models for image classification and recommendation systems. Proven track record in TensorFlow, PyTorch, and MLOps pipelines, delivering 15âŻ% accuracy gains on average.
ATS Tip
Lead with keywords like "Machine Learning", "Deep Learning", "TensorFlow" to ensure ATS match.
Detection Rules
objective contains fewer than 3 industryâspecific keywords
objective length exceeds 30 words
Resumly Tip
Swap the generic objective for a concise, impactâfocused summary that showcases your ML achievements.
Listing Projects Without Impact MetricsHIGH
Why it hurts
- Hiring managers canât gauge the value you delivered
- ATS may overlook vague bullet points
- Missed opportunity to demonstrate ROI
How to fix
- Add quantifiable results (e.g., accuracy improvement, latency reduction)
- Specify the scale of data or users
- Mention tools and frameworks used
â Before
- Developed a recommendation engine using collaborative filtering.
â After
- Designed and deployed a collaborativeâfiltering recommendation engine serving 200k daily users, increasing clickâthrough rate by 12âŻ% using Spark MLlib and AWS SageMaker.
ATS Tip
Include measurable outcomes and relevant tool names to trigger keyword matches.
Detection Rules
project bullet lacks numbers or percentages
bullet does not mention a specific ML framework
Resumly Tip
Turn each project description into a resultâoriented statement with clear metrics and technology stack.
Using Tables, Images, or Unusual FontsHIGH
Why it hurts
- Most ATS parsers cannot read tables or graphics
- Formatting may be stripped, causing loss of information
- Uncommon fonts can render incorrectly on recruiter screens
How to fix
- Use simple bullet points and plain text
- Stick to standard fonts like Arial or Calibri (11â12âŻpt)
- Avoid embedded images; describe visual work in text
â Before
<table><tr><td>Project</td><td>Result</td></tr><tr><td>Image Classifier</td><td>85% accuracy</td></tr></table>
â After
- Built an image classification model (ResNetâ50) achieving 85âŻ% topâ1 accuracy on a 1Mâimage dataset.
ATS Tip
Keep the resume ATSâfriendly by using standard headings and plainâtext sections.
Detection Rules
resume contains HTML tags
presence of <table> or <img> elements
font family not in [Arial,Calibri,Helvetica]
Resumly Tip
Replace tables and graphics with concise, keywordârich bullet points to ensure ATS readability.
Missing Relevant Keywords from Job DescriptionsHIGH
Why it hurts
- ATS filters out resumes lacking required terms
- Reduces chances of reaching a human reviewer
- Makes your expertise appear generic
How to fix
- Scrape 3â5 recent ML Engineer job ads
- Identify recurring terms (e.g., "MLOps", "GPU", "Kubernetes")
- Integrate those keywords naturally into experience and skills sections
â Before
Skills: Python, Machine Learning, Data Analysis
â After
Skills: Python, Machine Learning, Deep Learning, TensorFlow, PyTorch, MLOps, Docker, Kubernetes, GPU acceleration, Scikitâlearn
ATS Tip
Match exact phrasing from the job posting (e.g., "experience with Kubernetes" rather than "container orchestration").
Detection Rules
skills section contains fewer than 8 MLâspecific keywords
no mention of cloud platforms or container tools
Resumly Tip
Perform a keyword gap analysis and weave missing terms into your bullet points for higher ATS relevance.
Incorrect Date or Location FormattingLOW
Why it hurts
- ATS may misinterpret employment dates, causing timeline gaps
- Recruiters struggle to assess career progression
- Inconsistent formatting looks unprofessional
How to fix
- Use "MMM YYYY" format (e.g., Jan 2020 â Present)
- Place city and state on the same line as the company name
- Align dates to the right for readability
â Before
June 2018 â March 2020 Google AI, Mountain View
â After
Jun 2018 â Mar 2020 Google AI, Mountain View, CA
ATS Tip
Consistent date format helps ATS parse employment periods correctly.
Detection Rules
date strings contain full month names
location line missing state abbreviation
Resumly Tip
Standardize all dates to "MMM YYYY" and keep location details uniform across entries.
Formatting Guidelines
File Types: PDF, DOCX
Sections: Header, Professional Summary, Technical Skills, Professional Experience, Projects, Education, Certifications, Publications
Naming: FirstName_LastName_ML_Engineer_Resume
Consistency
Length: 1â2 pages for earlyâcareer, up to 3 pages for senior ML engineers
Date Format: MMM YYYY (e.g., Jan 2020)
Location Format: City, State, Country (optional)
Resume Quality Checklist
- Use a clear, keywordârich headline
- Add a concise, impactâfocused summary
- Quantify achievements in every bullet
- List technical skills in order of relevance
- Keep formatting ATSâfriendly (no tables or images)
- Standardize dates and locations
- Proofread for spelling and grammar
ATS Alignment Guide
Common ATS Systems: Lever, Greenhouse, iCIMS, Workday, SmartRecruiters
Keyword Strategy: Machine Learning, Deep Learning, TensorFlow, PyTorch, Scikitâlearn, NLP, Computer Vision, Model Deployment, MLOps, GPU acceleration
Heading Format: Use standard headings like "Professional Experience" and "Technical Skills" to ensure ATS parsing
Quick Fix Workshop
Paste your current resume text into the box below
- Replace generic summary with impactâfocused summary
- Add quantifiable results to each bullet
- Standardize date format to MMM YYYY
- Insert top ML keywords identified from job ads
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