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Deep Learning Jobs in Denton, TX (NOW HIRING)

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

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 ...

Expertise in machine learning, deep learning, and statistical modeling techniques (e.g., regression, time series, NLP, ensemble methods, neural networks). * Strong proficiency in Python, SQL, and ...

Expertise in machine learning, deep learning, and statistical modeling techniques (e.g., regression, time series, NLP, ensemble methods, neural networks). * Strong proficiency in Python, SQL, and ...

Senior GEN AI Engineer

Plano, TX · On-site

$100K - $137K/yr

The ideal candidate will have strong experience in Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), MLOps, and cloud-based AI platforms. The role involves working closely ...

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Deep Learning information

See Denton, TX salary details

$10.3K

$78.7K

$131.3K

How much do deep learning jobs pay per year?

As of Sep 12, 2026, the average yearly pay for deep learning in Denton, TX is $78,652.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,500.00 and $130,300.00 per year, depending on experience, location, and employer.

What is a deep learning job?

A Deep Learning job involves designing, developing, and optimizing neural networks to solve complex problems such as image recognition, natural language processing, and autonomous systems. Professionals in this field work with large datasets, neural network architectures, and frameworks like TensorFlow or PyTorch. They collaborate with data scientists, engineers, and researchers to improve model accuracy and efficiency. Deep Learning roles typically require strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration.

What are the typical daily responsibilities of a deep learning professional?

As a Deep Learning professional, your day-to-day tasks often include designing and training neural network models, preprocessing and analyzing large datasets, and evaluating model performance using various metrics. You may also participate in research activities, document your results, and collaborate with data scientists, engineers, or product teams to deploy machine learning solutions. Regular meetings for project updates, code reviews, and brainstorming sessions are common, as is staying updated on advances in the field. This dynamic environment offers both individual and team-based work, providing continuous learning and the opportunity to solve complex, real-world problems.

What are the key skills and qualifications needed to thrive in a deep learning position?

To thrive in Deep Learning, you need a solid understanding of machine learning theory, neural networks, mathematics (especially linear algebra and probability), and programming skills, typically backed by a degree in computer science, mathematics, or a related field. Familiarity with frameworks such as TensorFlow or PyTorch, experience with data preprocessing, and optionally industry-recognized certifications are advantageous. Strong analytical thinking, problem-solving skills, and the ability to communicate findings clearly are crucial soft skills. These abilities enable the design, implementation, and optimization of effective deep learning solutions in real-world applications.

What jobs use deep learning?

Jobs that use deep learning include roles such as machine learning engineer, data scientist, AI researcher, and computer vision engineer. These positions typically require skills in programming languages like Python, experience with deep learning frameworks such as TensorFlow or PyTorch, and knowledge of neural network architectures. They are common in industries like technology, healthcare, automotive, and finance, often involving tasks like image recognition, natural language processing, and predictive modeling.

What job categories do people searching Deep Learning jobs in Denton, TX look for?

The top searched job categories for Deep Learning jobs in Denton, TX are:

What cities near Denton, TX are hiring for Deep Learning jobs?

Cities near Denton, TX with the most Deep Learning job openings:

Infographic showing various Deep Learning job openings in Denton, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $78,652 per year, or $37.8 per hour.

Executive Director - Applied Artificial Intelligence Machine Learning

Plano, TX • On-site

JPMorgan Chase & Co.
Finance and Insurance • 10K+ employees

Other

Re-posted 4 days ago


JPMorgan Chase & Co. rating

7.9

Company rating: 7.9 out of 10

Based on 500 frontline employees who took The Breakroom Quiz


Job description

As an Applied AI/ML Executive Director within our dynamic team, you will apply your quantitative, data science, and analytical skills to complex problems. As a Machine Learning Director, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. You will collaborate with various teams and actively participate in our knowledge sharing community. We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you.

Job responsibilities
  • Develop advanced agentic AI solutions involving structured and unstructed data, casual analytics, machine learning, deep learning, reinforcement learning, and optimization.
  • Design robust agent architectures combining LLM reasoning with tools, structured data, and APIs spanning state, memory, and context management, plus loop engineering (plan/act/observe, verification, termination, and fallback/escalation).
  • Engineer reliable agent-driven workflows emphasizing correctness, traceability, and control-aware behavior (guardrails, approvals, auditable decision paths).
  • Build knowledge-centric reasoning layers, including knowledge graphs and hybrid retrieval (RAG + graph + structured sources) to improve grounding and accuracy.
  • Drive specification-driven development: author specs and contracts (schemas, validators, tool/skill interfaces) and build evaluation/regression harnesses.
  • Advance agent quality via recursive self-improvement through automated evaluation and critique loops, red-team feedback, skill/prompt instruction optimization, and outcome-driven dataset curation (human-in-the-loop as needed).
  • Coach and mentor AI/ML team members, setting a high bar for engineering rigor and research depth.
Required qualifications, capabilities, and skills
  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or with at least 5 years of industry experience or an MS with at least 7 years of industry or research experience in the field.
  • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
  • Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
  • Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
  • Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred qualifications, capabilities , and skills:
  • Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
  • Knowledge in search/ranking, Reinforcement Learning or Meta Learning
  • Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
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