Last month, my project partner from our Gold Price Prediction project asked for help with his resume. We’d just finished our machine learning minor project and Management, and he was applying to ML internships. But after two weeks of applications, he hadn’t gotten a single response.
“Can you look at my resume?” he asked, showing me a Word document.
The problem was immediately obvious. Generic Word template, inconsistent formatting, and our ML project—which involved Scikit-learn, TensorFlow, time-series analysis, and actual predictive modeling—was listed as just “Gold Price Prediction: Used Python and machine learning.”
That’s it. No details about the 15% accuracy improvement when we switched from traditional regression to LSTM networks. No mention of the data preprocessing pipeline we built. Nothing about the model evaluation or the results.
“Let me help you rebuild this in LaTeX,” I said. “And let’s actually showcase what we did in that project.”
Two weeks later: three interview calls. Same projects, same skills—just presented properly.
Here’s what I learned helping my project partner (and other friends since) create Machine Learning Engineer CVs: it’s not about fancy graphics or colors. It’s about clean typography, professional structure, and explaining your ML work in a way that shows you understand what you’re doing, not just that you ran some code.
Whether you’re a Computer Engineering student with ML projects like us, transitioning from software development, or trying to break into data science, this guide will show you how to build a resume that actually gets read.
Let me walk you through the exact process I used to help my project partner—and how you can apply it to your own Machine Learning Engineer CV.
Why I Chose LaTeX for ML Resumes (Real Reasons)
When my project partner asked why LaTeX instead of just fixing his Word resume, I showed him both versions side-by-side.
1. Professional Typography
LaTeX produces documents that look published, not amateur. The spacing, alignment, and fonts are optimized automatically. Among 100 generic Word templates, a LaTeX resume stands out.
Real recruiter feedback: My friend later told me a hiring manager commented, “Your resume actually looks professional. Most student resumes are obviously Word templates.”
2. Formatting Never Breaks
In Word, my partner spent an hour trying to align dates on the right side. Every time he edited text, dates would shift. In LaTeX, I wrote \hfill once and it worked perfectly forever.
Another example: Bullet points in Word would randomly change indentation. LaTeX keeps everything consistent automatically.
3. Easy to Update for Different Jobs
ML roles vary—some focus on model development, others on deployment and engineering. With LaTeX, you maintain one master resume and quickly emphasize different aspects.
How we do it: I showed my partner how to comment out certain projects or emphasize specific skills based on job descriptions. Takes 2 minutes vs rebuilding in Word.
4. ATS (Applicant Tracking Systems) Won’t Reject It
Most companies use software to scan resumes before humans see them. Word documents with tables, text boxes, or weird formatting often get parsed incorrectly. LaTeX produces clean, ATS-friendly PDFs.
Real problem: My partner’s Word resume listed his skills in a table. ATS couldn’t read it properly and auto-rejected him. LaTeX version with simple bullet points? Passed through every time.
5. Looks Better on Screen and Print
Recruiters view resumes on different devices—phones, tablets, monitors, printed paper. LaTeX PDFs look crisp and professional everywhere. Word documents? Sometimes formatting breaks depending on the viewer.
Structure of an Effective Machine Learning Engineer CV
Based on helping my project partner and seeing what got him interviews:
1. Header – Make Contact Info Clear
What to include:
- Full name (large, bold)
- Professional email address
- Phone number
- LinkedIn profile URL
- GitHub profile (non-negotiable for ML positions)
- Location (city, state)
- Portfolio website (if you have one)
Real example from our project:
\begin{center}
\textbf{\LARGE [Partner's Name]} \\
\vspace{0.2cm}
email@example.com | (555) 123-4567 \\
linkedin.com/in/name | github.com/username \\
Your Location
\end{center}
Critical lesson: Every ML recruiter checks GitHub. I made sure my partner’s repositories were organized with proper README files before sending applications. His Gold Price Prediction repo has our complete code, documentation, and results visualization.
2. Professional Summary – Hook Them Immediately
Bad summary (what my partner had): “Recent graduate seeking machine learning position.”
Better summary (what I helped him write): “Computer Engineering student with hands-on machine learning experience building predictive models. Developed time-series forecasting system using TensorFlow and Scikit-learn achieving 85% accuracy. Skilled in Python, data preprocessing, and model evaluation with experience in collaborative project development.”
Why it works:
- Specific technologies (TensorFlow, Scikit-learn)
- Quantifiable result (85% accuracy)
- Relevant skills (data preprocessing, model evaluation)
- Shows practical experience, not just theory
My rule: 2-3 sentences maximum. First sentence: who you are. Second: what you’ve done (with numbers). Third: what you’re good at.
3. Technical Skills – Organized and Honest
My partner originally listed every technology he’d ever heard of. I made him reorganize into clear categories showing actual proficiency.
LaTeX structure:
\section{Technical Skills}
\begin{itemize}[leftmargin=*]
\item \textbf{Programming Languages:} Python, Java, C++, SQL
\item \textbf{ML/AI Frameworks:} TensorFlow, Keras, Scikit-learn, Pandas, NumPy
\item \textbf{Data Tools:} Matplotlib, Seaborn, Jupyter Notebooks
\item \textbf{Development:} Git, VS Code, Google Colab
\item \textbf{Databases:} MySQL, PostgreSQL
\end{itemize}
What I removed: Technologies he’d only used once or didn’t really understand. Better to show depth in 5-6 tools than superficial knowledge of 20.
Honesty matters: He can explain TensorFlow and machine learning frameworks in interviews because we actually used them extensively. Listed technologies should pass the “explain it in detail” test.
4. Projects – Show What You Actually Built
This is where my partner’s resume needed the most work. He had:
Original (too vague): “Gold Price Prediction – Used machine learning to predict gold prices.”
Improved (what we wrote together):
\subsection{Gold Price Prediction Using Machine Learning | Minor Project | 2024}
\begin{itemize}[leftmargin=*]
\item Built time-series forecasting model predicting gold prices with 85\% directional accuracy
\item Collected and preprocessed 5 years of historical gold price data using Pandas and NumPy
\item Implemented multiple models (Linear Regression, LSTM) using Scikit-learn and TensorFlow
\item Achieved 15\% accuracy improvement by switching to LSTM networks over traditional methods
\item Performed exploratory data analysis and feature engineering with technical indicators
\item \textbf{Tech Stack:} Python, TensorFlow, Keras, Scikit-learn, Pandas, Matplotlib
\end{itemize}
Why this version works:
- Specific accuracy metric (85%)
- Shows data handling skills (preprocessing, EDA)
- Multiple technologies demonstrated
- Comparison showing improvement (15% better)
- Technical depth without being overwhelming
Key lesson: Every bullet point should answer “What did you do?” and “What was the result?” Numbers and specific technologies make it real.
5. Education – Include Relevant Coursework
Structure:
\section{Education}
\subsection{Bachelor of Science in Computer Engineering | 2021-2025}
Your College
\begin{itemize}[leftmargin=*]
\item \textbf{Relevant Coursework:} Machine Learning, Data Structures, Algorithms,
Database Management, Statistics
\item \textbf{Minor Project:} Gold Price Prediction using ML (Team of 4)
\end{itemize}
GPA consideration: My partner’s GPA was 3.4. I advised leaving it off since it’s under 3.5. If yours is 3.5+, include it. If not, skip it.
Relevant coursework matters: Shows you have theoretical foundation, not just random project work. List courses directly related to ML/data science.
6. Experience (If You Have It)
My partner had an internship doing web development. I helped him reframe one bullet point to highlight relevant skills:
Original: “Developed web applications using JavaScript and React”
Improved: “Built data visualization dashboards displaying real-time analytics, processing 10K+ user interactions daily”
Point: Even non-ML experience can be positioned to show relevant skills (data handling, visualization, performance).
Complete LaTeX Resume Template (What I Actually Used)
Here’s the exact LaTeX template I created for my project partner, adapted for general use:
\documentclass[11pt]{article}
\usepackage[margin=0.75in]{geometry}
\usepackage{enumitem}
\usepackage{titlesec}
\usepackage{hyperref}
% Section formatting
\titleformat{\section}{\large\bfseries}{}{0em}{}[\titlerule]
\titleformat{\subsection}{\bfseries}{}{0em}{}
% Remove page numbers
\pagestyle{empty}
\begin{document}
% Header
\begin{center}
\textbf{\LARGE Your Name} \\
\vspace{0.2cm}
\href{mailto:your.email@email.com}{your.email@email.com} | (555) 123-4567 \\
\href{https://linkedin.com/in/yourname}{linkedin.com/in/yourname} |
\href{https://github.com/yourname}{github.com/yourname} \\
City, Country
\end{center}
% Professional Summary
\section{Professional Summary}
Computer Engineering student with hands-on machine learning experience building predictive models. Developed time-series forecasting system achieving 85\% accuracy using TensorFlow and Scikit-learn. Skilled in Python, data preprocessing, model evaluation, and collaborative development.
% Technical Skills
\section{Technical Skills}
\begin{itemize}[leftmargin=*]
\item \textbf{Programming Languages:} Python, Java, SQL, C++
\item \textbf{ML/AI Frameworks:} TensorFlow, Keras, Scikit-learn, Pandas, NumPy
\item \textbf{Data Visualization:} Matplotlib, Seaborn
\item \textbf{Tools:} Git, Jupyter Notebooks, Google Colab, VS Code
\item \textbf{Databases:} MySQL, PostgreSQL
\end{itemize}
% Projects
\section{Projects}
\subsection{Gold Price Prediction Using Machine Learning | Minor Project | 2024}
\begin{itemize}[leftmargin=*]
\item Built time-series forecasting model predicting gold prices with 85\% directional accuracy
\item Collected and preprocessed 5 years of historical data using Pandas and NumPy
\item Implemented multiple models: Linear Regression, Random Forest, LSTM networks
\item Achieved 15\% accuracy improvement switching from traditional ML to deep learning (LSTM)
\item Performed exploratory data analysis and feature engineering with technical indicators
\item Collaborated in team of 2, using Git for version control and code management
\item \textbf{Tech Stack:} Python, TensorFlow, Keras, Scikit-learn, Pandas, Matplotlib
\end{itemize}
\subsection{[Your Other Project] | Personal/Academic | 2024}
\begin{itemize}[leftmargin=*]
\item [What you built and what problem it solved]
\item [Specific technologies and methodologies used]
\item [Quantifiable results or outcomes]
\item [Technical challenges overcome]
\item \textbf{Tech Stack:} [List relevant technologies]
\end{itemize}
% Education
\section{Education}
\subsection{Bachelor of Science in Computer Engineering | 2021-2025}
Your College
\begin{itemize}[leftmargin=*]
\item \textbf{Relevant Coursework:} Machine Learning, Data Structures, Algorithms,
Database Management Systems, Statistics, Data Mining
\item \textbf{Projects:} Gold Price Prediction (ML), [Other relevant projects]
\end{itemize}
% Optional: Certifications
\section{Certifications}
\begin{itemize}[leftmargin=*]
\item [Certification Name] - [Provider] - [Year]
\item [Example: TensorFlow Developer Certificate - Google - 2024]
\end{itemize}
% Optional: Achievements
\section{Additional Information}
\begin{itemize}[leftmargin=*]
\item \textbf{Languages:} English (Fluent), Nepali (Native)
\item \textbf{Interests:} Contributing to open-source ML projects, Kaggle competitions
\end{itemize}
\end{document}
What makes this template work:
- Clean structure: Clear sections, consistent formatting
- ATS-friendly: No tables, no complex formatting
- Highlights ML work: Projects section is detailed and prominent
- Quantifiable: Numbers throughout (85% accuracy, 15% improvement, 5 years data)
- One page: For students/entry-level, one page is ideal
How to Customize This Template (Step-by-Step)
Here’s exactly how I helped my partner personalize this template:
Step 1: Replace Personal Information
% Change these Your Name → [Partner's actual name] your.email@email.com → [His email] (555) 123-4567 → [His phone] linkedin.com/in/yourname → [His LinkedIn] github.com/yourname → [His GitHub]
Important: Make sure GitHub has clean, documented repositories before linking it.
Step 2: Customize Professional Summary
Write 2-3 sentences covering:
- Who you are (student/recent grad + field)
- What you’ve done (specific project with result)
- What you’re skilled in (relevant technologies)
My partner’s actual summary: “Computer Engineering student with machine learning project experience in time-series forecasting. Developed gold price prediction model using TensorFlow achieving 85% directional accuracy. Proficient in Python, data preprocessing, and collaborative development using Git.”
Step 3: List Only Skills You Can Explain
Go through each technology and ask: “Can I explain how I used this in an interview?”
If yes: keep it.
If no: remove it.
Example: My partner knew Pandas well (we used it extensively). He removed Apache Spark (just heard of it, never used).
Step 4: Detail Your Projects
For each project, write bullet points covering:
- What: What did you build?
- How: What technologies did you use?
- Results: What were the outcomes? (Use numbers)
- Challenges: What problems did you solve?
Template:
- [Action verb] [what you built] [achieving/resulting in] [quantified result] - [Technical implementation details with specific technologies] - [Key learnings or improvements made]
Step 5: Add Education Details
Include:
- Degree and major
- Institution name and location
- Years (graduation year or expected graduation)
- Relevant coursework (4-6 courses related to ML/data science)
- GPA only if 3.5+
Step 6: Optional Sections
Add if you have:
- Certifications (Coursera, edX, etc.)
- Publications or research
- Awards or achievements
- Languages spoken
- Professional memberships
Skip if you don’t – one strong page beats a weak two-page resume.
Compiling Your LaTeX Resume
Option 1: Overleaf (Easiest)
- Go to Overleaf
- Create free account
- New Project → Blank Project
- Copy-paste the LaTeX code
- Click “Recompile”
- Download PDF
Why I recommend this for beginners: No installation needed, works everywhere, automatically handles packages. Perfect for getting started with LaTeX document creation.
My partner’s workflow: He keeps his resume on Overleaf so he can update it from college computers, home, or anywhere.
Option 2: Local Setup
Windows: Install MiKTeX
Mac: Install MacTeX
Linux: sudo apt-get install texlive-full
Editor: TeXstudio (easiest) or VS Code with LaTeX Workshop extension
Compile:
pdflatex resume.tex
Why local setup: Faster compilation, works offline, no file size limits.
Common Mistakes I Fixed in My Partner’s Resume
Mistake 1: Too Vague
Bad: “Used machine learning for predictions”
Good: “Built LSTM model predicting gold prices with 85% accuracy using TensorFlow”
Why: Specific technologies and metrics prove you actually did the work.
Mistake 2: Listing Responsibilities Instead of Achievements
Bad: “Responsible for data preprocessing”
Good: “Preprocessed 5 years of historical data, handling missing values and outliers using Pandas”
Why: Shows what you accomplished, not just what you were supposed to do.
Mistake 3: Including Irrelevant Information
Removed:
- High school achievements
- Unrelated hobbies (“enjoy watching movies”)
- Every course ever taken
- Skills not related to ML/software development
Why: One page is precious. Every line should support your candidacy for ML roles.
Mistake 4: Poor Project Descriptions
Bad: “Gold Price Prediction – Group project using Python”
Good: Full detailed description (shown in template above)
Why: Projects are your proof of ability. Explain them thoroughly.
Mistake 5: Inconsistent Formatting
Problems:
- Some dates on right, some on left
- Different bullet point styles
- Inconsistent spacing
- Random bold/italic usage
Solution: LaTeX handles formatting automatically if you use consistent commands.
Mistake 6: No GitHub Link
Reality: For ML positions, recruiters check GitHub. No GitHub = immediate disadvantage.
What I made my partner do:
- Create GitHub account
- Upload Gold Price Prediction code with README
- Add documentation explaining the project
- Include visualizations of results
- Link it prominently on resume
Tailoring Your Resume for Different Applications
My partner applied to both ML-focused roles and general software positions. Here’s how we adapted his resume:
For ML/Data Science Roles
Emphasize:
- Machine learning projects (detailed descriptions)
- Data analysis and preprocessing skills
- Model evaluation metrics
- Familiarity with ML frameworks
Professional summary: “Computer Engineering student with machine learning experience in predictive modeling and data analysis…”
For Software Engineering Roles
Emphasize:
- Programming skills (Java, C++, not just Python)
- Software development projects (web apps, mobile apps)
- Problem-solving and algorithms
- Version control and collaboration
Professional summary: “Computer Engineering student with full-stack development experience and machine learning project background…”
How to manage multiple versions:
In LaTeX, I showed him how to comment out sections:
% For ML roles, include this
\subsection{Detailed ML Project Description}
% content...
% For software roles, use shorter version
% \subsection{Brief ML Project Mention}
% condensed content...
Keep one master file, create copies for different focuses, or use version control branches.
What Happened After (Real Results)
Before the LaTeX resume:
- 15 applications sent
- 0 responses
- 2 weeks of frustration
After the LaTeX resume:
- Same 15 companies, updated application
- 3 interview calls in first week
- 2 more over next two weeks
- 1 internship offer eventually
What changed:
- Same projects, same experience
- Better presentation and structure
- Quantified achievements
- Professional appearance
- ATS-friendly format
His feedback: “I wish I’d done this from the start. The LaTeX version just looks more serious.”
Additional Resources for ML Job Seekers
Based on what helped my partner:
Learning ML
- Coursera Machine Learning (Andrew Ng)
- Fast.ai courses
- Kaggle competitions for practice
- Academic papers on arXiv
Building Projects
- Kaggle datasets
- UCI Machine Learning Repository
- Government open data portals
- Create your own datasets (web scraping, APIs)
LaTeX Help
- What is LaTeX? – Start here if new
- LaTeX for CVs and Resumes – Detailed guide
- Common LaTeX errors and fixes – Debugging
- Writing math equations in LaTeX – For technical content
For Interview Prep
- LeetCode for coding problems
- “Hands-On Machine Learning” book
- Understanding ML algorithms and evaluation metrics deeply
- Being able to explain your projects in detail
- Practice AI tool assessment to understand what companies look for
My Advice for ML Resume Success
From helping my partner and others:
1. Projects over everything
For students without work experience, projects prove your ability. Make them detailed, documented, and impressive.
2. GitHub is your portfolio
Clean repos with good README files are as important as the resume itself. I made my partner spend a whole day organizing his GitHub before applying.
3. Quantify everything
85% accuracy, 5 years of data, 15% improvement—numbers make achievements concrete and believable.
4. Be honest about skills
Only list technologies you can discuss in interviews. One friend claimed “expert in PyTorch” but couldn’t explain basic concepts. Didn’t get the job.
5. One strong page beats two weak pages
My partner’s original resume was two pages with filler. One focused page got him interviews.
6. Customize for each application
Takes 10 minutes to adjust emphasis based on job description. Shows you actually read the requirements.
7. Get feedback
Show your resume to seniors, professors, or people working in ML. My partner got valuable suggestions from a teaching assistant who works part-time in data science.
8. Keep it updated
Add new projects, skills, or experiences immediately. Don’t wait until you’re desperately applying and need to update everything at once.
Common Questions (From Helping Multiple Friends)
Q: How long should my ML resume be?
One page for students and entry-level. Only go to two pages if you have 5+ years of relevant experience.
Q: Should I include my GPA?
Only if it’s 3.5 or higher. Below that, skip it. Nobody will ask.
Q: What if I don’t have ML work experience?
Projects, internships, and coursework count. My partner had zero professional ML experience but strong project work. Building personal AI projects or contributing to open-source ML repositories helps too.
Q: GitHub is messy, should I still link it?
Clean it up first. Delete embarrassing repos, add README files to good ones, organize by relevance. Then link it.
Q: Should I include all technologies I’ve touched?
No. Only technologies you can confidently discuss in interviews. Quality over quantity.
Q: What about online certifications?
Include if from reputable platforms (Coursera, edX, Google, AWS). Skip if from unknown sites.
Q: How to handle group projects?
Be honest about it being team work (like I did: “Team of 2”). Explain your specific contributions in interviews.
Q: Should I explain gaps in education?
Not on resume unless specifically asked. Save explanations for cover letters or interviews if needed.
Final Thoughts
Building an effective Machine Learning Engineer CV isn’t about fancy formatting or lying about skills. It’s about clearly presenting what you’ve actually done in a professional way that makes recruiters want to interview you.
LaTeX helps with the professional presentation. But the content—your projects, your skills, your achievements—that’s what actually matters.
My project partner’s resume got better not because LaTeX is magic, but because rebuilding in LaTeX forced us to think carefully about every line: Is this relevant? Is this specific? Does this show capability?
Whether you use LaTeX or Word, ask those questions about every item on your resume.
For LaTeX specifically: yes, there’s a learning curve. But once you have a working template, maintaining and updating your resume is actually easier than Word. And the output looks undeniably more professional.
If you’re a Computer Engineering student with ML projects, or someone trying to break into data science, spend a weekend building your resume properly. It’s time well invested.
Want to learn more about using LaTeX for professional documents? Check out these guides on Deadloq:
Good luck with your applications!
For more guides on programming, machine learning, and technical skills, visit Deadloq.