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Reinforcement Learning Engineer Jobs in Atlanta, GA

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize ... Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing ...

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize ... Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing ...

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

AI Engineer - Remote

Atlanta, GA · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

Senior Machine Learning Engineer I

Atlanta, GA · On-site

$117K - $155K/yr

ABOUT THIS POSITION We are seeking a highly skilled and innovative Senior ML Engineer with a ... unsupervised, and reinforcement learning techniques, as well as deep learning architectures.

Senior Machine Learning Engineer I

Atlanta, GA · On-site

$117K - $155K/yr

ABOUT THIS POSITION We are seeking a highly skilled and innovative Senior ML Engineer with a ... unsupervised, and reinforcement learning techniques, as well as deep learning architectures.

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

Create reinforcement learning environments for software engineering tasks. * Design tasks involving bug fixing, feature development, refactoring, and performance optimization . * Build deterministic ...

... Engineer Job Type: Contractor (~15 hours/week) Location: Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning ...

... Engineer Job Type: Contractor (~15 hours/week) Location: Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning ...

Research Scientist Senior

Atlanta, GA · On-site +1

$94K - $120K/yr

... reinforcement learning solutions for healthcare optimization and intelligent decision systems. * Builds and operationalizes production-grade ML systems. * Partners closely with engineering ...

Research Scientist Senior

Atlanta, GA · On-site +1

$94K - $120K/yr

... reinforcement learning solutions for healthcare optimization and intelligent decision systems. * Builds and operationalizes production-grade ML systems. * Partners closely with engineering ...

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Showing results 1-20

Reinforcement Learning Engineer information

See Atlanta, GA salary details

$36.5K

$111.4K

$184.1K

How much do reinforcement learning engineer jobs pay per year?

As of Sep 1, 2026, the average yearly pay for reinforcement learning engineer in Atlanta, GA is $111,392.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,800.00 and $145,700.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

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 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 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 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 job categories do people searching Reinforcement Learning Engineer jobs in Atlanta, GA look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Atlanta, GA are:

What cities near Atlanta, GA are hiring for Reinforcement Learning Engineer jobs?

Cities near Atlanta, GA with the most Reinforcement Learning Engineer job openings:

Infographic showing various Reinforcement Learning Engineer job openings in Atlanta, GA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, 1% Contract, and 1% Nights. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $111,422 per year, or $53.6 per hour.

Machine Learning Engineer - Remote

YO AI Labs

Atlanta, GA • Remote

$80 - $120/hr

Full-time

Posted 5 days ago


Job description

Senior Software Engineer

Job Type: Contractor (~15 hours/week)
Location: Remote

Job Summary

We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software engineering tasks using Model Context Protocol (MCP) tools.

You will design reproducible environments, deterministic verification, and reference solutions for tasks such as bug fixing, feature implementation, codebase refactoring, and performance optimization. No prior AI experience is required.

Key Responsibilities
  • Create reinforcement learning environments for software engineering tasks.
  • Design tasks involving bug fixing, feature development, refactoring, and performance optimization.
  • Build deterministic verification systems and golden reference solutions.
  • Evaluate AI agents' ability to reason through complex codebases and use MCP tools effectively.
  • Develop realistic, reproducible software engineering scenarios.
  • Ensure tasks accurately measure coding ability, problem-solving, and tool usage.
  • Document solutions and provide clear technical feedback.
Required Skills
  • Strong proficiency in Python 3, Java, Rust, C++, or TypeScript.
  • Strong understanding of algorithms and data structures.
  • Experience with bug fixing and debugging complex software issues.
  • Proven experience in feature implementation and codebase refactoring.
  • Strong knowledge of performance optimization and tuning.
  • Excellent written and verbal communication.
  • Strong attention to detail.
Preferred Qualifications
  • Experience working with large-scale or distributed codebases.
  • Familiarity with AI/ML systems is a plus but not required.
  • Experience with rigorous code reviews and software engineering best practices.
  • Experience working effectively in remote or cross-functional teams.
Hiring Process
  1. Submit an application and screening questions.
  2. Complete an AI interview (~30 minutes).
  3. Complete a technical assessment, if required.
  4. Hiring Manager review.
Compensation

Compensation is output-based, with payment provided per task that meets project specifications. Minimum weekly submission requirements may apply.

Availability

Selected experts should be prepared to begin their first tasks within 24–48 hours of completing onboarding.