Data Science & AI · Mid-Senior (4+ yrs)

Computer Vision & NLP Engineer Resume

This sample shows how to connect computer vision and language models to measurable product improvements, from image pipelines to text classification. A strong resume for this role makes the data, model choices, deployment work, and evaluation results clear to both engineering and applied research teams.

Resume check

What recruiters and tracking systems look for. A guide, not a prediction.

100 / 100Complete

100%
  • Contact20 / 20
  • Summary20 / 20
  • Experience30 / 30
  • Skills20 / 20
  • Education10 / 10

Contact details

Put these at the top; recruiters and tracking systems look for them first.

Optional. In the US, UK and Canada resumes usually have no photo.

51 words

One job per paragraph; start each achievement on a new line with “-”.

11 skills

Separate skills with commas, for example: Excel, SQL, project planning.

More sections

Projects, certifications, languages or anything else that supports your application.

Selected projects
Tools

Alex Morgan

Computer Vision & NLP Engineer

  • alex.morgan@example.com
  • +1 555 0100
  • Denver, CO

Summary

Computer Vision & NLP Engineer with 5 years of experience building production ML features for a B2B logistics platform. Strengths include image preprocessing, transformer fine-tuning, and model evaluation, with a record of improving document extraction and package-damage review workflows. Experienced in deploying PyTorch services and monitoring model quality after release.

Experience

Computer Vision & NLP Engineer at Pineglass Parcel Technologies, Denver, CO (2023 – Present) - Built a PyTorch and OpenCV damage-detection pipeline for warehouse photos, routing about 1,200 images per day and reducing manual review volume by 18%. - Fine-tuned a Hugging Face token-classification model for shipping-document extraction, raising field-level F1 from 0.86 to 0.91 on a held-out evaluation set. - Deployed versioned inference services with Docker and Kubernetes, cutting median image-processing time from 1.8 seconds to 1.2 seconds per image. Machine Learning Engineer at Ridgeway Applied Language Labs, Boulder, CO (2021 – 2023) - Developed OCR and named entity recognition workflows for invoices using Tesseract and BERT, shortening average exception triage time by 22% across two customer teams. - Evaluated YOLO object-detection models on a 14,000-image labeled dataset, identifying camera and lighting gaps that improved recall by 7 percentage points.

Education

Bachelor of Science in Computer Science — Aspen Vale Institute of Computing, Fort Collins, CO (2021)

Skills

  • Computer vision
  • Natural language processing
  • PyTorch
  • OpenCV
  • Hugging Face Transformers
  • Object detection
  • OCR
  • Named entity recognition
  • Model evaluation
  • Docker
  • Kubernetes

Selected projects

• Package-damage detection: compared YOLO model variants and documented recall by damage type. • Shipping-document extraction: created an evaluation set for OCR and entity extraction across varied scans.

Tools

• Python, PyTorch, OpenCV, Hugging Face Transformers, Tesseract • Docker, Kubernetes, MLflow, PostgreSQL, Git

How to write a Computer Vision & NLP Engineer resume

Lead with shipped model work

Put your current role and strongest production work near the top. State what the model handled, the data scale or operating setting, and what changed after release, such as review volume, latency, or extraction quality. Separate prototypes from features used by customers or operations; hiring teams need to see whether you have supported models beyond a notebook.

Show how you evaluated models

Name the evaluation method and the metric that fits the task: precision and recall for detection, F1 for extraction, or latency for an inference service. Give the dataset size or source when you can, and explain whether results came from a held-out set or production monitoring. Avoid unsupported claims that a model is accurate without stating what you measured.

Make your stack specific

List the frameworks, model families, and deployment tools you used hands-on, such as PyTorch, OpenCV, Hugging Face Transformers, Docker, or Kubernetes. Tie each tool to a project or achievement instead of listing every package you have tried. Mention cloud platforms or vector databases only when they were part of your actual work and are relevant to the job posting.

Link a focused portfolio

A concise portfolio can show a reproducible vision or NLP project, evaluation approach, and a clear explanation of tradeoffs. Link to code or a demo you are allowed to share, and remove customer data, credentials, and proprietary model details. Keep academic papers, tutorials, and side projects distinct from production work so readers can quickly understand your level of ownership.

Common keywords

Skills and tools often listed for this role. Use only the ones you have, in the job advert's wording. Click one to copy it.

Action verbs

BuiltTrainedDeployedReducedImprovedAutomatedEvaluated

Questions about this role

How long should a Computer Vision & NLP Engineer resume be?

For around five years of experience, two pages is usually reasonable if the second page contains relevant project or technical detail. Keep the strongest production work, model evaluation, and deployment results on the first page. Early coursework and unrelated jobs can usually be shortened or removed unless they show directly relevant skills.

Should I include a portfolio or GitHub link?

Include one if it demonstrates relevant work that is public and easy to review, such as a reproducible project, evaluation notebook, or technical write-up. Explain the task, data source, metrics, and your contribution. Do not upload customer data, employer code, or material covered by confidentiality agreements; a sanitized description is safer for that work.

Which certifications should I list for this role?

Computer Vision & NLP Engineer roles generally do not require a universal professional license or certification in the United States. List a relevant cloud or machine learning certification only if you earned it, name the issuer and date when useful, and keep it below substantial project experience. Do not present course completion as a professional certification.

Can I move into this role from data science or software engineering?

Yes. Emphasize projects that show you can build and evaluate vision or language models, handle real data, and integrate inference into an application. For a data science background, add deployment and monitoring examples; for software engineering, show model training, task-appropriate metrics, and dataset handling. Label personal or academic projects clearly and describe your individual contribution.