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Reinforcement Learning Engineer Jobs in Texas (NOW HIRING)

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/yr

... reinforcement learning. Experience with cloud platforms such as Google Cloud Platform (GCP ... GCP Professional Machine Learning Engineer certification is required. Experience with version ...

Cymertek

Machine Learning Engineer

San Antonio, TX

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/yr

Requirements: • Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a ... learning, and reinforcement learning. • Experience with cloud platforms such as Google Cloud ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Apple

Machine Learning Engineer

Austin, TX

$132K - $244K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ... Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and ...

Apple

Machine Learning Engineer

Austin, TX · On-site

$132K - $244K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ... Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and ...

Apple

Machine Learning Engineer

Austin, TX · On-site

$132K - $244K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ... Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and ...

webAI Inc

Staff R&D AI Engineer

Austin, TX · On-site

Architect and implement reinforcement learning systems for sequential decision-making, including ... Collaborate with engineering teams to integrate AI models into applications and validate ...

Braze

Forward-Deployed Data Scientist II

Austin, TX · Hybrid

$98K - $164K/yr

Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms * Customer collaborator: Comfortable working ...

Apple

Senior Machine Learning Engineer

Austin, TX

$181K - $318K/yr

... Engineer. This role will assist our Online Retail Decision Automation team by helping to research ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$181K - $318K/yr

... Engineer. This role will assist our Online Retail Decision Automation team by helping to research ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$181K - $318K/yr

... Engineer. This role will assist our Online Retail Decision Automation team by helping to research ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

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Reinforcement Learning Engineer information

See Texas salary details

$35.4K

$107.9K

$178.4K

How much do reinforcement learning engineer jobs pay per year?

As of Jun 11, 2026, the average yearly pay for reinforcement learning engineer in Texas is $107,946.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,300.00 and $141,100.00 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 Texas? For Reinforcement Learning Engineer jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Engineer jobs in Texas look for? The top searched job categories for Reinforcement Learning Engineer jobs in Texas are:
What cities in Texas are hiring for Reinforcement Learning Engineer jobs? Cities in Texas with the most Reinforcement Learning Engineer job openings:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Texas as of June 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $107,946 per year, or $51.9 per hour.
Senior ML Engineer

Senior ML Engineer

Resolve Tech Solutions

Addison, TX • On-site

$101K - $138K/yr

Full-time

Posted 2 hours ago

Job description

Responsibilities:
Develop machine learning models and algorithms to address business needs.
Collaborate with data scientists and software engineers to design and implement scalable and efficient solutions.
Clean, preprocess, and analyze large datasets to extract meaningful insights.
Deploy machine learning models into production environments and monitor their performance.
Continuously improve model accuracy and performance through experimentation and optimization.
Stay up-to-date with the latest advancements in machine learning and related technologies.
Communicate findings and results to stakeholders in a clear and concise manner.


Requirements:
Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field.
2~5 years of experience in machine learning, data science, or a related field.
Proficiency in programming languages such as Python, Java, or Scala.
Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.
Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Experience with cloud platforms such as Google Cloud Platform (GCP), including services like BigQuery, Cloud Storage, and AI Platform.
GCP Professional Machine Learning Engineer certification is required.
Experience with version control systems such as Git.
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration skills.


Preferred Qualifications:
Master's degree or higher in Computer Science, Engineering, Mathematics, or a related field.
Experience with distributed computing frameworks such as Apache Spark.
Familiarity with containerization and orchestration technologies such as Docker and Kubernetes.
Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau.
Experience with natural language processing (NLP) or computer vision (CV) techniques.
Experience with continuous integration and continuous deployment (CI/CD) pipelines.
Contributions to open-source projects or participation in relevant communities.

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