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Reinforcement Learning With Human Feedback Jobs in Decatur, AL

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Reinforcement Learning With Human Feedback information

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$25

$38

$65

How much do reinforcement learning with human feedback jobs pay per hour?

As of Jul 3, 2026, the average hourly pay for reinforcement learning with human feedback in Decatur, AL is $38.15, according to ZipRecruiter salary data. Most workers in this role earn between $27.74 and $49.57 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning with Human Feedback (RLHF) Engineer, and why are they important?

To excel as a Reinforcement Learning with Human Feedback (RLHF) Engineer, you need a strong background in machine learning, reinforcement learning theory, statistics, and typically an advanced degree in computer science or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like Ray RLlib), and experience with data collection and annotation systems are essential. Excellent problem-solving abilities, communication skills, and teamwork help you collaborate with researchers, data annotators, and other engineers. These skills enable you to design and implement RLHF systems that are robust, scalable, and aligned with human values.

What is the difference between Reinforcement Learning With Human Feedback vs Reinforcement Learning Engineer?

AspectReinforcement Learning With Human FeedbackReinforcement Learning Engineer
CredentialsTypically requires knowledge of machine learning, AI, and data analysisRequires similar credentials in machine learning, programming, and AI
Work EnvironmentResearch labs, AI development teams, tech companiesDevelopment teams, research labs, tech firms
Industry UsageUsed in AI training, human-in-the-loop systems, and model refinementDesigning, implementing, and optimizing reinforcement learning algorithms

Reinforcement Learning With Human Feedback focuses on improving AI models through human input, while Reinforcement Learning Engineers develop and deploy these algorithms. Both roles require strong machine learning skills and often work in similar environments, but their core responsibilities differ in application and focus.

What is Reinforcement Learning with Human Feedback?

Reinforcement Learning with Human Feedback (RLHF) is a machine learning technique where AI agents are trained not only through automated reward signals but also by incorporating feedback from humans. This approach helps align the agent’s behavior with human preferences, values, or safety requirements by allowing humans to guide or correct the learning process. RLHF is commonly used in developing advanced AI systems, such as language models, to ensure their outputs are helpful, safe, and aligned with user expectations. The process often involves human evaluators ranking or scoring the AI's responses, which are then used to fine-tune the model’s behavior.

What are the typical collaborations involved for a Reinforcement Learning with Human Feedback (RLHF) specialist within a machine learning team?

As an RLHF specialist, you often work closely with data scientists, machine learning engineers, and domain experts to design effective feedback mechanisms and reward models. Collaboration with annotation teams or subject matter experts is common, as high-quality human feedback is crucial for training robust RLHF models. You may also partner with product managers and UX researchers to ensure that the models align with user needs and ethical considerations. Regular cross-functional meetings and code reviews help maintain alignment and foster innovation across teams.
What are popular job titles related to Reinforcement Learning With Human Feedback jobs in Decatur, AL? For Reinforcement Learning With Human Feedback jobs in Decatur, AL, the most frequently searched job titles are:
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Reinforcement Learning With Human Feedback jobs near you
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 10 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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