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Reinforcement Learning Engineer Jobs in Alabama (NOW HIRING)

Becton, Dickinson and Company

It takes the imagination and passion of all of us-from design and engineering to the manufacturing ... Equip frontline leaders and regional teams with tools and reinforcement mechanisms to sustain ...

All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Alabama

Reinforcement Learning Engineer information

See Alabama salary details

$34.4K

$105K

$173.6K

How much do reinforcement learning engineer jobs pay per year?

As of Jun 28, 2026, the average yearly pay for reinforcement learning engineer in Alabama is $105,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,200.00 and $137,300.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Alabama? For Reinforcement Learning Engineer jobs in Alabama, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Engineer jobs in Alabama look for? The top searched job categories for Reinforcement Learning Engineer jobs in Alabama are:
What cities in Alabama are hiring for Reinforcement Learning Engineer jobs? Cities in Alabama with the most Reinforcement Learning Engineer job openings:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Alabama as of June 2026, with employment types broken down into 64% Full Time, 27% Part Time, and 9% Temporary. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $105,018 per year, or $50.5 per hour.
Mid-Level AI / Machine Learning Software Engineer

Mid-Level AI / Machine Learning Software Engineer

Modern Technology Solutions, Inc.

Huntsville, AL • On-site

$112K - $135K/yr

Full-time

Posted 5 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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