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7 AI/ML Engineer Intern - Architectural Modeling Resume Templates with Example

By Ria · Last Updated On October 24, 2026
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This guide shows you how to create an AI/ML Engineer Intern - Architectural Modeling resume that makes your relevant work easy to understand. You will learn how to organize project evidence and choose a clear format for this specific role.

Strong resumes connect drawing data preparation with computer vision methods and model evaluation. Use concise bullets to explain your work with DWG preprocessing, rasterization, dataset annotation, Python, PyTorch, TensorFlow, and OpenCV.

The examples present the same AI/ML Engineer Intern - Architectural Modeling resume in 7 layouts. Compare the image previews to see how each format arranges projects, technical skills, education, and other relevant sections, then open an example in the editor to customize it.

What Makes a Great AI/ML Engineer Intern - Architectural Modeling Resume in 2026

A strong resume uses clear section headings and concise bullets to show work with architectural drawings, computer vision, and deep learning models. Put the most relevant projects and technical skills where readers can find them quickly.

Include accurate terms for methods and tools you have used, such as DWG preprocessing, rasterization, data augmentation, dataset annotation, edge detection, PyTorch, TensorFlow, and OpenCV. Add specific results when available, and explain how you evaluated models or documented experiments.

Note: Resume examples are for educational purposes only. Use only information that accurately reflects your experience and qualifications. References to real organizations do not imply affiliation or endorsement.

#1 Simple One Column Format Resume Example

A single-column body keeps the resume on one clear reading path, a practical choice for straightforward review.

 
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Why This Format Works

  • A single reading path helps reviewers follow experience, projects, and education in order.
  • Skills and credentials remain part of the same straightforward text flow.
  • Consistent section progression gives project evidence room to read as connected work.

Best For

  • Single-column reading preference
  • Sequential experience review
  • Project-rich resumes
  • Text-first applications
  • Continuous section order

Resume Keywords

  • computer vision
  • Build, train, and evaluate deep learning models for drawing element detection and segmentation
  • Trained and evaluated U-Net, YOLO, Mask R-CNN, and ViT approaches in PyTorch and TensorFlow, comparing segmentation quality and detection results against a held-out set of drawings.
  • Built a computer vision prototype to detect and segment walls, openings, and structural symbols in architectural drawings
  • DWG preprocessing
  • Dataset annotation
  • Image rasterization
  • Data augmentation
  • Python
  • PyTorch
  • Jupyter Notebook
  • Git
  • NumPy
  • Pandas
  • Shapely
See all keywords
 

#2 Simple Two Column Format Resume Example

Two columns organize the resume into distinct areas, a practical choice for readers who prefer grouped information.

 
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Why This Format Works

  • Two-column organization can separate supporting details from the main career narrative.
  • Distinct content areas help reviewers locate skills, education, and credentials efficiently.
  • Project and experience sections remain easier to compare across the resume.

Best For

  • Side-by-side information review
  • Resumes with supporting sections
  • Experience paired with credentials
  • Readers comparing section groups
  • Grouped resume content

#3 Plain One Column with Shaded Labels Resume Example

The one-column body presents content in a single sequence, a practical choice for readers who want a direct top-to-bottom review.

 
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Why This Format Works

  • The continuous column gives experience and project entries a clear top-to-bottom order.
  • Skills and education stay within one reading sequence rather than competing columns.
  • ATS software can follow the straightforward text order through key sections.

Best For

  • Direct top-to-bottom review
  • Single-path resume reading
  • ATS-focused submissions
  • Experience in clear sequence
  • Projects alongside education
 

#4 One Column Format with Photo Right Resume Example

A one-column body with a photo on the right suits readers who want a direct text path and a visible portrait.

 
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Why This Format Works

  • The single-column body preserves a direct reading order for experience and project work.
  • Skills and education follow the same vertical path, keeping section changes easy to track.
  • Placing the photo on the right leaves the resume's text structure distinct.

Best For

  • Right-side photo preference
  • One-column resume reading
  • Direct experience review
  • Portrait with text-focused content
  • Projects in reading order
 

#5 Clean Two Column Sidebar Format Resume Example

A two-column layout groups resume information into separate areas, a practical choice for readers who like a clear division of content.

 
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Why This Format Works

  • Separate columns give supporting sections room beside the experience narrative.
  • Reviewers can locate skills, education, and credentials without losing the work-history sequence.
  • Project details remain part of the same resume while information is grouped across columns.

Best For

  • Two-column reading preference
  • Resumes with varied sections
  • Experience alongside supporting details
  • Work history with project evidence
  • Clearly divided content
 
 

#6 Two Column Format Resume Example

Two columns divide the resume into distinct reading areas, a practical choice for reviewing several types of information side by side.

 
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Why This Format Works

  • Two columns let reviewers scan different resume sections without following one long text stream.
  • Career history and project evidence can be read alongside supporting information.
  • Grouped section areas help skills, education, and credentials stand apart in review.

Best For

  • Side-by-side section review
  • Several types of resume content
  • Career history with project evidence
  • Skills and education in distinct areas
  • Readers seeking divided content
 

#7 Photo Header Two Column Format Resume Example

A two-column body with a photo combines grouped resume content with a visible portrait for readers who value both.

 
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Why This Format Works

  • Two-column grouping helps reviewers move between experience and supporting resume sections.
  • Skills and credentials can remain easy to locate within the divided content areas.
  • The photo adds a visual element, while experience and skills stay readable as text.

Best For

  • Two-column content organization
  • Photo with grouped sections
  • Experience beside supporting details
  • Skills and credentials in divided areas
  • Readers who prefer a portrait
 
 
 

Why Build Your AI/ML Engineer Intern - Architectural Modeling Resume with ResumeInMinutes

ResumeInMinutes helps you organize project work, education, technical skills, and research experience in a resume that is easy to scan and customize. Clear sections make it simpler to show how your work relates to architectural drawing analysis and computer vision.

  • ATS-friendly section structure
  • Clear project and experience sections
  • Role-relevant keyword guidance
  • Organized technical skills
  • Image previews for comparison
  • Examples you can customize in the editor

Use the examples to review how a resume can present model training, classical computer vision methods, experiment findings, and collaboration with research and engineering peers. Choose a layout that keeps your most relevant evidence easy to review.

 

See Also

 

Frequently Asked Questions

Are these AI/ML Engineer Intern - Architectural Modeling resume templates ATS-friendly?

They use clear headings and organized sections to support easy scanning. Include relevant terms such as architectural drawing analysis, DWG preprocessing, dataset annotation, computer vision, and model evaluation when they accurately describe your experience.

Are these different resumes or layouts of one example?

The guide shows one AI/ML Engineer Intern - Architectural Modeling resume example in different layouts. The image previews let you compare how each one arranges the content.

What should an AI/ML Engineer Intern - Architectural Modeling resume include?

Include a focused summary, education, relevant projects or experience, technical skills, certifications, and applicable recognition. Describe drawing data preparation, model training and evaluation, computer vision methods, and experiment results with clear, specific bullets.

Which resume layout works well for this role?

A simple layout with clear headings makes projects, education, and technical skills easy to find. Choose a format that presents your relevant evidence without crowding the page.

Can project work help show relevant experience?

Yes. Explain the task, methods, tools, and evaluation steps for each relevant project. For architectural modeling work, you can describe drawing preparation, element detection or segmentation, and how you reviewed model results.

Should I include a photo on my resume?

Include a photo only if it is customary or expected in your country, region, or industry. For U.S. applications, a resume without a photo is generally the safer choice.

ATS score and resume checks

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ATS Score 93%

How we calculate this score

  • Keywords: 60% of the total. AI extracts keywords from your job description. If you do not provide one, a standard job description for the title matching your resume is used. This is not a match to a specific vacancy.
  • Text-based resume: 20%. The text resume assessed here receives 100 for this part.
  • Resume quality: 20%. We start at 100 and subtract points for issues found by our resume checks, down to a minimum of zero.
  • Keyword coverage = round(100 × confirmed matches ÷ total keywords). Exact matches and supported equivalents verified by AI receive full credit; unsupported keywords receive none. Live text checks use the saved equivalents and do not request a new AI review. Without keywords, no overall ATS score is available.
  • Final score = 60% of keyword coverage + 20% of the text score + 20% of the quality score, rounded to a whole number. Without keywords, no overall ATS score is available.

What this score means

Our scores are estimates, not employer ratings. They may contain errors and do not guarantee accurate parsing, a ranking, an interview, or a job. Check your document against the employer’s requirements. You are responsible for relying on this score.

Terms and Conditions

Score weights: Keywords 60% · Text based resume 20% · Resume quality 20%

Keyword coverage: 30 of 34 (88%) · Resume quality: 100%

Resume quality 100% · Weight 20% · Issues (0)

Details
  • ✓Name present: Martin Mclaughlin
  • ✓Email format: martin.mclaughlin@example.com
  • ✓Phone format: (626) 555-XXXX
  • ✓Experience 1: Job title: Architectural Drawing Segmentation Capstone Project
  • ✓Experience 1: Company name: University of Southern California Structural AI Project
  • ✓Experience 1: Work location: Los Angeles, CA
  • ✓Experience 1: Employment dates: Jan 2026 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Architectural Plan CV Research Project
  • ✓Experience 2: Company name: USC Computer Vision Research Lab
  • ✓Experience 2: Work location: Los Angeles, CA
  • ✓Experience 2: Employment dates: Sep 2025 - Dec 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Architectural Drawing Analysis Course Project
  • ✓Experience 3: Company name: University of Southern California
  • ✓Experience 3: Work location: Los Angeles, CA
  • ✓Experience 3: Employment dates: Jan 2025 - May 2025
  • ✓Experience 3: Description or achievements: Present
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

No issues found by the current checks.

Text Based Resume 100% · Weight 20%

Parsed text
Martin Mclaughlin
(626) 555-XXXX
martin.mclaughlin@example.com
martinmclaughlin
Pasadena, CA 91103
SUMMARY
Computer science student pursuing a Bachelor of Science at the University of Southern California, with hands-on project experience in computer vision for architectural drawings. Build, train, and evaluate deep learning models for drawing element detection and segmentation, and prepare DWG data through rasterization, augmentation, and annotation. Apply classic image analysis methods and Python-based tools to test model results, inspect geometry, and document experiments. Collaborate with engineering and research peers on a Structural AI Agent concept for structural engineers, sharing benchmark findings and integration considerations. Approach uncertain technical questions through problem solving and iterative experimentation, with adaptability to fast-paced development environments.
EDUCATION
Bachelor of Science
2027
University of Southern California
Los Angeles, CA
TECHNICAL SKILLS
Programming Languages: Python, C++, Java
Machine Learning Frameworks: PyTorch, TensorFlow, Keras
Computer Vision Libraries: OpenCV, scikit-image, Pillow
Scientific Computing: NumPy, SciPy, Matplotlib
Data Analysis: Pandas, Polars, Seaborn
Geometry and CAD: Shapely, ezdxf, pythonOCC
Notebook Environments: Jupyter Notebook, JupyterLab, Google Colab
Version Control: Git, GitHub, GitLab
Deep Learning Models: U-Net, YOLO, Mask R-CNN
Image Annotation Tools: CVAT, Label Studio, LabelMe
Data Formats: DWG, DXF, PNG
SKILLS
Architectural drawing analysis
Computer vision
Deep learning
Element segmentation
Model evaluation
DWG preprocessing
Dataset annotation
Image rasterization
Data augmentation
Experiment documentation
Benchmark analysis
Technical communication
Iterative experimentation
Research collaboration
Geometric analysis
EXPERIENCE
Architectural Drawing Segmentation Capstone Project
Jan 2026 - Present
University of Southern California Structural AI Project
Los Angeles, CA
Built a computer vision prototype to detect and segment walls, openings, and structural symbols in architectural drawings, preparing rasterized DWG samples and annotated datasets for model training.
Trained and evaluated U-Net, YOLO, Mask R-CNN, and ViT approaches in PyTorch and TensorFlow, comparing segmentation quality and detection results against a held-out set of drawings.
Applied OpenCV edge detection, contour analysis, shape recognition, and geometric transformations, using Shapely to validate and refine extracted geometry.
Used Python, Jupyter Notebook, NumPy, and Pandas to track experiments, inspect errors, and document benchmark results for review with engineering and research peers.
Prototyped a model handoff to a Structural AI Agent workflow, presenting integration considerations and test findings to a student project review group.
Architectural Plan CV Research Project
Sep 2025 - Dec 2025
USC Computer Vision Research Lab
Los Angeles, CA
Prepared architectural plan images for a supervised research study by rasterizing DWG files, standardizing image dimensions, and applying augmentation to broaden the training examples.
Developed Python and OpenCV baselines using edge detection, contour detection, shape analysis, and geometric transformations to identify drawing elements before deep learning experiments.
Trained CNN and U-Net models with PyTorch, then compared predictions using annotated samples to identify recurring segmentation errors and refine data labels.
Organized datasets and analysis in Pandas and NumPy, maintained reproducible notebooks and Git history, and presented experiment methods and results at a lab review.
Architectural Drawing Analysis Course Project
Jan 2025 - May 2025
University of Southern California
Los Angeles, CA
Created a small annotated drawing dataset and a Python pipeline for image cleanup, rasterization, and geometric feature inspection, documenting each processing step in Jupyter Notebook.
Tested OpenCV contour and edge detection methods for architectural shape recognition, using Shapely to check polygon boundaries and summarize geometric properties.
Compared baseline outputs with manually reviewed examples and shared limitations and next-step recommendations in a course presentation.
LEADERSHIP, AWARDS & RECOGNITION
USC Viterbi Dean's List, Fall 2025.
Presented a computer vision project at the Undergraduate Research Poster Session in 2025.
Coordinated the student project group in 2026, organizing peer review sessions and presentation preparation.
Mentored peers through the USC Computer Science study group in 2025.
CERTIFICATIONS
DeepLearning.AI, Convolutional Neural Networks
2025
Coursera, Introduction to Computer Vision and Image Processing
2025
PROFESSIONAL AFFILIATIONS
Student member, Association for Computing Machinery (ACM), participating in the professional community for computing students.
Student member, IEEE Computer Society, engaging with a professional community focused on computer science and engineering.

Matched keywords (30)

  • computer visionOriginal keyword in JD: Currently pursuing a bachelor's or master's degree in Computer Science OR Artificial Intelligence OR Computer Vision OR a related field
  • Build, train, and evaluate deep learning models for drawing element detection and segmentationOriginal keyword in JD: Deep learning computer vision model development
  • Trained and evaluated U-Net, YOLO, Mask R-CNN, and ViT approaches in PyTorch and TensorFlow, comparing segmentation quality and detection results against a held-out set of drawings.Original keyword in JD: Deep learning model training and evaluation
  • Built a computer vision prototype to detect and segment walls, openings, and structural symbols in architectural drawingsOriginal keyword in JD: Architectural drawing element detection and segmentation
  • DWG preprocessing
  • Dataset annotation
  • Image rasterization
  • Data augmentation
  • Python
  • PyTorchOriginal keyword in JD: PyTorch OR TensorFlow OR OpenCV
  • Jupyter Notebook
  • Git
  • NumPy
  • Pandas
  • Shapely
  • Developed Python and OpenCV baselines using edge detection, contour detection, shape analysis, and geometric transformationsOriginal keyword in JD: Classic computer vision algorithms
  • edge detection
  • contour analysis
  • shape analysis
  • geometric transformations
  • CNN
  • U-Net
  • YOLO
  • Mask R-CNN
  • document experimentsOriginal keyword in JD: Experimental documentation
  • document benchmark resultsOriginal keyword in JD: Benchmarking
  • Technical communication
  • problem solving
  • Approach uncertain technical questions through problem solving and iterative experimentation, with adaptability to fast-paced development environments.Original keyword in JD: Adaptability to fast-paced, uncertain environments
  • Collaborate with engineering and research peers on a Structural AI Agent concept for structural engineersOriginal keyword in JD: Collaboration with engineers and researchers

Missing keywords (4)

  • Research and development
  • Computer vision model integration
  • Structural engineering
  • Curiosity and experimentation
Disclaimer: All names, images, contact information, employment histories, achievements, security clearances, military credentials, and other qualifications shown in this content are fictional and used for illustrative purposes only. Any resemblance to actual persons, living or deceased, is purely coincidental.

Resume and cover-letter examples and recommendations in this article reflect general best practices and professional experience and are provided for guidance only. Results may vary based on role, industry, region, and hiring organization.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that resume or cover-letter guidance, examples, recommendations, or related information are accurate, complete, current, error-free, or suitable for any particular purpose. You are responsible for independently reviewing and verifying the information before relying on it.

ATS and Cover-Letter Scores: ATS scores are estimates calculated using our own formula, informed by publicly available guidance on resume formatting and keyword matching. They are not scores issued or endorsed by an ATS provider or employer. Cover-letter scores are also our own assessments. Parsing, weighting, screening criteria, and results vary between systems, employers, job descriptions, and document formats. No score guarantees successful parsing, a particular ranking, an interview, or employment. Use scores as general guidance and independently check your documents against the employer’s requirements. To the fullest extent permitted by applicable law, we accept no liability for errors in scores or parsing, or losses arising from reliance on these assessments. Nothing in this notice excludes liability or limits consumer rights that cannot lawfully be excluded or limited.

Terms and Conditions
 
 
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