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Deep Learning Developer Jobs in Dallas, TX (NOW HIRING)

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 ...

JDA

Sr. Machine Learning Engineer

Dallas, TX · On-site +1

$103K - $142K/yr

JOB TITLE: Sr. Machine Learning Engineer LOCATION: Coppell, TX (Hybrid role, may work from home ... deep learning frameworks such as TensorFlow, PyTorch, or JAX; * 3 years in applying machine ...

PB consulting

Generative AI Engineer

Grapevine, TX · On-site

We are seeking a skilled Generative AI Engineer to design, develop, and deploy advanced AI ... Develop and optimize machine learning, deep learning, and NLP models for enterprise applications.

New

PB consulting

Generative AI Engineer

Irving, TX

We are seeking a skilled Generative AI Engineer to design, develop, and deploy advanced AI ... Develop and optimize machine learning, deep learning, and NLP models for enterprise applications.

New

ApTask

Python developer

Dallas, TX · On-site

$50 - $68.75/hr

Senior Full Stack Engineer with Python and Go (Golang) Note:- Only Python Developer with AI skills ... PostgreSQL/MySQL/MongoDB Git, CI/CD AI MLFrameworks CrewAI LangChain Machine Learning Deep Learning ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/yr

... deep learning, and reinforcement learning. Experience with cloud platforms such as Google Cloud ... GCP Professional Machine Learning Engineer certification is required. Experience with version ...

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Deep Learning Developer information

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How much do deep learning developer jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for deep learning developer in Dallas, TX is $38.03, according to ZipRecruiter salary data. Most workers in this role earn between $32.36 and $42.31 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a deep learning developer?

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What is a deep learning developer?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

What is the difference between Deep Learning Developer vs Machine Learning Engineer?

AspectDeep Learning DeveloperMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What are some common challenges deep learning developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.
What cities near Dallas, TX are hiring for Deep Learning Developer jobs? Cities near Dallas, TX with the most Deep Learning Developer job openings:
Deep Learning Developer jobs near you
Infographic showing various Deep Learning Developer job openings in Dallas, TX as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $79,096 per year, or $38 per hour.

Machine Learning Engineer

UNAVAILABLE

Frisco, TX • On-site

$140 - $190/hr

Other

Posted 4 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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