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Intern Computer Vision Deep Learning Engineer Jobs in Coppell, TX

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

Fine-tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale. * Develop vision-language models and Mixture of Experts architectures, from ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX

$101K - $138K/yr

... deep learning, and reinforcement learning. Experience with cloud platforms such as Google Cloud ... Experience with natural language processing (NLP) or computer vision (CV) techniques. Experience ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/yr

... unsupervised learning, deep learning, and reinforcement learning. • Experience with cloud ... computer vision (CV) techniques. • Experience with continuous integration and continuous ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/yr

... unsupervised learning, deep learning, and reinforcement learning. • Experience with cloud ... computer vision (CV) techniques. • Experience with continuous integration and continuous ...

Waabi

Research Engineer

Dallas, TX · On-site +1

$122K - $215K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

Waabi

Research Engineer

Dallas, TX · On-site +1

$122K - $215K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

Waabi

Research Engineer

Dallas, TX · On-site +1

$122K - $215K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

Northern Base

Generative AI Engineer

Irving, TX · On-site

Hiring Alert | Generative AI Engineer Location: Irving, TX (Onsite) Employment Type: Full-Time ... Implement advanced deep learning, NLP, computer vision, and Generative AI techniques * Evaluate AI ...

Waabi

Senior / Staff Perception Engineer

Dallas, TX · On-site

$158K - $269K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

Waabi

Senior / Staff Perception Engineer

Dallas, TX · On-site

$158K - $269K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

Waabi

Senior / Staff Perception Engineer

Dallas, TX · On-site +1

$158K - $269K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

Tata Consultancy Service Limited

Engineer

Irving, TX · On-site

$80K - $90K/yr

Desired Competencies (Technical/Behavioral Competency) Must-Have • Programming languages: Python ... deep learning, natural language processing, and computer vision, tailored to specific needs 5 ...

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All JobsIntern Computer Vision Deep Learning Engineer JobsIntern Computer Vision Deep Learning Engineer Jobs in Coppell, TX

Intern Computer Vision Deep Learning Engineer information

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How much do intern computer vision deep learning engineer jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for intern computer vision deep learning engineer in Coppell, TX is $15.73, according to ZipRecruiter salary data. Most workers in this role earn between $13.32 and $17.74 per hour, depending on experience, location, and employer.

What does an intern computer vision deep learning engineer do?

An Intern Computer Vision Deep Learning Engineer assists in developing and improving algorithms that enable computers to interpret and understand visual information from the world, such as images and videos. They often work on tasks like image classification, object detection, and facial recognition using deep learning frameworks like TensorFlow or PyTorch. Interns typically help with data collection, model training, evaluation, and sometimes deployment, all under the guidance of experienced team members. This role is a great opportunity to gain hands-on experience in machine learning and computer vision while contributing to real-world projects.

What are the key skills and qualifications needed to thrive as an intern computer vision deep learning engineer?

To thrive as an Intern Computer Vision Deep Learning Engineer, you need a solid understanding of machine learning fundamentals, computer vision concepts, and proficiency in programming languages like Python, often supported by coursework or personal projects. Familiarity with deep learning frameworks such as TensorFlow or PyTorch and experience with image processing libraries like OpenCV are typically expected. Strong problem-solving abilities, curiosity, and effective teamwork skills help interns excel in fast-paced research and development environments. These skills are essential for contributing to innovative projects and adapting to the rapidly evolving field of computer vision.

What types of projects or tasks can I expect to work on as an intern computer vision deep learning engineer?

As an Intern Computer Vision Deep Learning Engineer, you can expect to contribute to projects involving image or video analysis, such as object detection, image classification, or facial recognition. Your daily tasks might include data preprocessing, annotating datasets, training and evaluating deep learning models, and assisting with model optimization for deployment. You’ll often work closely with senior engineers and researchers, gaining hands-on experience with real-world datasets and cutting-edge frameworks. Collaboration with cross-functional teams, such as software developers and product managers, is common to ensure your models address practical business needs.

What is the difference between Intern Computer Vision Deep Learning Engineer vs Intern Machine Learning Engineer?

AspectIntern Computer Vision Deep Learning EngineerIntern Machine Learning Engineer
Required SkillsComputer vision, deep learning, CNNs, Python, TensorFlow/PyTorchMachine learning, algorithms, Python, scikit-learn, TensorFlow/PyTorch
Work EnvironmentResearch labs, tech companies, startups focusing on image/video analysisTech companies, research labs, startups working on diverse ML applications
Industry UsagePrimarily in computer vision projects like object detection, image segmentationBroader ML projects including predictive modeling, NLP, recommendation systems

Intern Computer Vision Deep Learning Engineers focus on image and video analysis using deep learning techniques, while Intern Machine Learning Engineers work on a wider range of ML applications. Both roles require strong Python skills and familiarity with deep learning frameworks, but their project focus and industry applications differ.

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Intern Computer Vision Deep Learning Engineer jobs near you
Infographic showing various Intern Computer Vision Deep Learning Engineer job openings in Coppell, TX as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, and 4% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $32,713 per year, or $15.7 per hour.

Machine Learning Engineer

UNAVAILABLE

Frisco, TX • On-site

$140 - $190/hr

Other

Posted 16 days ago

Job description

Overview

Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems. In this role, you will design, develop, and deploy state-of-the-art computer vision and language models that power scalable, real-world solutions. You’ll work with large-scale image and video data, building and optimizing production-grade vision systems while contributing clean and modular code to shared repositories.

As part of our AI team, you’ll collaborate closely with engineering teams to deliver high-impact features for our growing SaaS platform. The ideal candidate brings hands‑on experience deploying computer vision and language models in production and applying MLOps best practices on cloud platforms.

Responsibilities
  • Fine‑tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale.
  • Develop vision‑language models and Mixture of Experts architectures, from experimental design through production deployment.
  • Architect Retrieval‑Augmented Generation (RAG) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation.
  • Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade computer vision models, with an emphasis on clean, modular, maintainable code.
  • Contribute to our machine learning repositories and optimize models for performance, scalability, and real‑time inference across edge and cloud environments.
  • Drive performance optimization and scalability of ML systems across edge and cloud environments.
  • Collaborate with cross‑functional teams to integrate computer vision solutions into end‑to‑end products, translating research outcomes into measurable platform impact.

This list of responsibilities might not cover everything you'll end up doing.

Qualifications
  • 5+ years of hands‑on machine learning experience, with deep specialization in computer vision and a proven track record of shipping models to production.
  • Master's degree required (Ph.D. preferred) in Computer Science, Machine Learning, or a closely related field.
  • Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.
  • Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) able to own the full model lifecycle from experimentation through production monitoring.
  • Experience building and deploying LLM-based systems and Retrieval‑Augmented Generation (RAG) pipelines, including vector store integration and retrieval evaluation.
  • Strong communicator who can translate complex research findings into actionable decisions for engineering and product stakeholders.
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