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Remote Reinforcement Learning Jobs in Dallas, TX

CosmoProf

MANAGER FIELD TRAINING

Plano, TX · Remote

... learning, and field reinforcement tools). * Partner with Leadership to identify training needs ... Proven track record in curriculum design and facilitation for remote and field-based teams.

Sally Beauty

MANAGER FIELD TRAINING

Plano, TX · Remote

... learning, and field reinforcement tools). * Partner with Leadership to identify training needs ... Proven track record in curriculum design and facilitation for remote and field-based teams.

All JobsReinforcement Learning JobsRemote Reinforcement Learning JobsRemote Reinforcement Learning Jobs in Dallas, TX

Remote Reinforcement Learning information

See Dallas, TX salary details

$10.9K

$83K

$138.5K

How much do remote reinforcement learning jobs pay per year?

As of Aug 21, 2026, the average yearly pay for remote reinforcement learning in Dallas, TX is $82,982.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,200.00 and $137,500.00 per year, depending on experience, location, and employer.

What is a remote reinforcement learning?

A Remote Reinforcement Learning job involves developing and applying reinforcement learning algorithms while working from a location outside of a traditional office environment. Professionals in this field focus on creating systems where agents learn optimal behaviors through trial and error, often using feedback from their environment. These jobs typically require expertise in machine learning, programming, and mathematics, and are commonly found in industries like robotics, gaming, and autonomous systems. Working remotely allows researchers and engineers to collaborate with global teams using digital tools and platforms.

What are the key skills and qualifications needed to thrive as a remote reinforcement learning engineer?

To thrive as a Remote Reinforcement Learning Engineer, you need a strong background in machine learning, statistics, and programming (especially Python), often supported by an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and RL-specific libraries like OpenAI Gym, along with experience using cloud computing platforms, is typically required. Excellent problem-solving skills, self-motivation, and effective remote communication help individuals excel in distributed teams. These skills ensure the successful design, implementation, and deployment of reinforcement learning solutions while collaborating efficiently in a remote work environment.

What are common challenges faced when working remotely in a reinforcement learning role and how can they be addressed?

Working remotely in a Reinforcement Learning role often involves overcoming communication barriers with cross-functional teams, managing large-scale experiments without on-site resources, and staying updated with rapidly evolving research. To address these challenges, it's important to establish regular check-ins with colleagues, utilize cloud-based platforms for experiment management, and participate in virtual seminars or journal clubs. Developing strong self-motivation and time management skills is also crucial to maintain productivity in a remote environment.

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

AspectRemote Reinforcement Learning
Required CredentialsMaster's or PhD in Computer Science, AI, or related fields; knowledge of RL algorithms
Work EnvironmentResearch-focused, experimental, often involves simulation and algorithm development
Employer & Industry UsageTech companies, research labs, AI startups focusing on autonomous systems
Common Search & Comparison IntentUnderstanding specialized AI roles, research focus, and technical skills

Remote Reinforcement Learning specialists focus on developing algorithms that enable machines to learn through trial and error in simulated or real environments. In contrast, Remote Machine Learning Engineers typically work on deploying and optimizing various machine learning models across applications. While both roles require strong programming skills and knowledge of AI, reinforcement learning emphasizes decision-making processes, whereas machine learning engineering covers a broader range of models and deployment strategies.

What are popular job titles related to Remote Reinforcement Learning jobs in Dallas, TX?

For Remote Reinforcement Learning jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Remote Reinforcement Learning jobs in Dallas, TX look for?

The top searched job categories for Remote Reinforcement Learning jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Remote Reinforcement Learning jobs?

Cities near Dallas, TX with the most Remote Reinforcement Learning job openings:

Remote Reinforcement Learning jobs near you
Infographic showing various Remote Reinforcement Learning job openings in Dallas, TX as of August 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 100% Remote job distribution, with an average salary of $82,982 per year, or $39.9 per hour.

Software Engineer - AI Specialist | Remote

GigWorld Talent Solutions

Dallas, TX • On-site, Remote

Full-time

Re-posted 9 days ago

Job description

Job Description Software Engineer - AI Specialist | Remote Permanent, Full-Time We are supporting an innovative technology firm dedicated to building AI-driven solutions that drive efficiency and transformation across industries. We are seeking a highly skilled Software Engineer specializing in Artificial Intelligence to develop, optimize, and deploy AI-powered applications. Responsibilities: Design, develop, and implement AI models and machine learning algorithms.

Collaborate with cross-functional teams to integrate AI solutions into existing platforms. Optimize AI models for performance, scalability, and efficiency. Research and apply the latest advancements in AI and deep learning.

Develop and maintain data pipelines and AI-driven analytics systems. Ensure AI models are robust, ethical, and aligned with best practices. Troubleshoot and improve AI-based applications as needed.

Stay updated on emerging AI trends and technologies. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience in AI and machine learning development.

Proficiency in programming languages such as Python, Java, or C++. Strong understanding of AI frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, etc.). Experience with natural language processing (NLP), computer vision, or predictive analytics

Knowledge of data science methodologies and model evaluation techniques. Experience with cloud platforms and AI services (AWS, Azure, GCP). Ability to work independently and collaboratively in a fast-paced environment.

Preferred Qualifications: Experience with reinforcement learning and generative AI models. Familiarity with AI ethics, bias mitigation, and explainability techniques. Contribution to open-source AI projects.

Understanding of big data technologies and distributed computing. Benefits: Competitive salary and performance-based incentives. Flexible work schedule and remote work opportunities.

Professional development and continuous learning resources. Opportunity to work with a passionate and innovative team in a fast-growing industry.

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