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Deep Learning Jobs in Alabama (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 ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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Deep Learning information

See Alabama salary details

$10K

$76K

$126.9K

How much do deep learning jobs pay per year?

As of Jun 20, 2026, the average yearly pay for deep learning in Alabama is $76,033.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,300.00 and $126,000.00 per year, depending on experience, location, and employer.

What jobs use deep learning?

Jobs that use deep learning include roles such as deep learning engineer, machine learning engineer, data scientist, AI researcher, and computer vision engineer. These positions typically require skills in programming languages like Python, experience with frameworks such as TensorFlow or PyTorch, and a strong understanding of neural networks and data analysis.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning engineer, AI research director, or chief AI officer, often requiring advanced skills in deep learning, data science, and programming. These roles usually involve leadership, strategic planning, and expertise in tools like TensorFlow or PyTorch, with compensation reflecting experience, impact, and industry demand.

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 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 engineers make $500,000?

Senior deep learning engineers with extensive experience, advanced skills in neural networks, and expertise in frameworks like TensorFlow or PyTorch can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level often requires a strong educational background, specialized certifications, and a track record of impactful projects.

What job makes $10,000 a month without a degree?

In the field of deep learning, roles such as freelance AI consultant or specialized machine learning engineer can potentially earn $10,000 or more per month through project-based work or high-demand expertise. Success typically requires strong skills in programming, neural networks, and experience with tools like TensorFlow or PyTorch, often gained through self-study or online courses rather than formal degrees.

What are the key skills and qualifications needed to thrive in the Deep Learning position, and why are they important?

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 are the most commonly searched types of Deep Learning jobs in Alabama? The most popular types of Deep Learning jobs in Alabama are:
Mid-Level AI / Machine Learning Software Engineer

Mid-Level AI / Machine Learning Software Engineer

Modern Technology Solutions, Inc.

Huntsville, AL โ€ข On-site

$112K - $135K/yr

Full-time

Posted 27 days ago


Job description

We are seeking a Mid-Level AI / Machine Learning Software Engineer to support development of scalable data analysis and machine learning capabilities across large datasets and real-time data streams. The role focuses on designing, implementing, and optimizing machine learning models and data pipelines using Python and modern deep learning frameworks.
The ideal candidate has strong programming fundamentals, hands-on model development experience, and is comfortable working with large structured and unstructured datasets in production environments.
Primary Responsibilities
  • Design, develop, and maintain Python-based data processing and analytics solutions
  • Implement and optimize machine learning and deep learning models
  • Work with large datasets and streaming data sources
  • Develop reusable data structures and efficient algorithms for analysis workflows
  • Build and evaluate models for classification, prediction, and pattern recognition
  • Integrate AI/ML capabilities into software systems and pipelines
  • Collaborate with software engineers, data engineers, and analysts to deploy solutions
  • Perform model validation, performance tuning, and debugging
  • Document architecture, implementation, and usage of developed tools

Required Qualifications
  • 3+ years of professional software development experience
  • Strong Python development skills
  • Experience working with large datasets and/or streaming data
  • Proficiency in machine learning and deep learning frameworks:
  • PyTorch
  • TensorFlow
  • Keras
  • Hugging Face Transformers
  • Understanding of machine learning concepts and model architectures, including:
  • Decision Trees / Random Forests
  • LSTM / sequence models
  • Experience implementing, training, and evaluating ML models
  • Knowledge of data structures, algorithms, and performance optimization
  • Familiarity with version control (Git) and collaborative development workflows

Desired / Preferred Qualifications
  • Experience with Retrieval-Augmented Generation (RAG)
  • Experience with Model Context Protocols (MCP) or similar agent/tool interaction frameworks
  • Experience with GPU acceleration and CUDA architecture
  • Drivers, runtime, and APIs
  • Experience with deep learning and reinforcement learning libraries
  • Experience building or consuming real-time data pipelines
  • Data visualization and exploratory analysis (Matplotlib, Seaborn, Plotly, etc.)
  • Familiarity with model deployment and inference optimization
  • Experience working in containerized or distributed environments

Education
  • Bachelor's degree (or working toward a degree) in Computer Science, Data Science, Engineering, Mathematics, or related field
  • (Equivalent practical experience considered)

Nice-to-Know Technologies
  • Linux development environments
  • Jupyter notebooks
  • Docker or container basics
  • Basic command line usage

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