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Reinforcement Learning Engineer Jobs in Frisco, TX

IT America Inc

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

Agentic AI Engineer Lead Location: Dallas, TX & Basking ridge, NJ - Day one Onsite ( 5 days ... Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved ...

Elevance Health

Research Scientist Senior

Grand Prairie, TX · On-site +1

$93K - $118K/yr

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

CCS INC

Gen AI Engineer

Plano, TX · On-site

$40 - $50/hr

Implement prompt engineering, instruction tuning, and reinforcement learning from human feedback (RLHF). * Integrate GenAI capabilities into enterprise platforms using APIs, SDKs, and orchestration ...

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Showing results 21-40

All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Frisco, TX

Reinforcement Learning Engineer information

See Frisco, TX salary details

$35.6K

$108.4K

$179.2K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for reinforcement learning engineer in Frisco, TX is $108,442.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,700.00 and $141,800.00 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 are popular job titles related to Reinforcement Learning Engineer jobs in Frisco, TX?

For Reinforcement Learning Engineer jobs in Frisco, TX, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Frisco, TX look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Frisco, TX are:

What cities near Frisco, TX are hiring for Reinforcement Learning Engineer jobs?

Cities near Frisco, TX with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Frisco, TX as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $108,442 per year, or $52.1 per hour.

Machine Learning Engineer, GenAI/ML - Vice President

J.P. Morgan

Plano, TX

Full-time

Medical, Retirement

Posted 20 days ago

Job description

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

Join a dynamic and diverse team that engineers large-scale, complex, and resilient technology solutions that drive our global business.

As a Machine Learning Engineer, GenAI/ML - Vice President within Banking & Wealth Management, Consumer & Community banking Home Lending Technology team, you will be tasked with the design, construction, and maintenance of our AIOps solution. This role demands a profound knowledge of AI/ML technologies, IT infrastructure, and platform engineering.

Job Responsibilities:

  • Lead the design, development, and deployment of generative AI solutions, ensuring alignment with business objectives and technical requirements. 
  • Demonstrate deep expertise in generative AI technologies, contributing to the development of POCs and evaluating new methodologies to enhance AI capabilities.
  • Exhibit strong proficiency in Java or Python, with the ability to architect and build complex AI models from scratch. Ensure the delivery of secure, high-quality production code.
  • Utilize experience with React or Angular to create intuitive user interfaces for AI applications. Conduct thorough code reviews to maintain high standards of code quality.
  • Leverage AWS experience to implement best practices in AI integration, ensuring quality, security, and efficiency across AI projects.
    Identify and implement opportunities to automate processes and enhance the operational stability of generative AI applications and systems.
  • Actively participate in communities of practice to promote the adoption and awareness of new generative AI technologies, fostering a culture of continuous innovation.

Required qualifications, capabilities, and skills:

  • Minimum 7+ years of strong proficiency in Python or Java, with the ability to architect and build complex AI models from scratch.
  • Two years of experience in generative AI development and prompt engineering.
  • Proven experience in system design, application development, testing, and operational stability in AI projects.
  • Minimum 5+ years of strong experience with React or Angular.
  • Minimum 2+ years of AWS experience.
  • Understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, applied to AI projects.
  • Experience with LLM models and agentic tools.

Preferred qualifications, capabilities, and skills:

  • Experience with AI model optimization and performance tuning to ensure efficient and scalable AI solutions.
  • Familiarity with data engineering practices to support AI model training and deployment.
  • Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
  • Experience with AI/ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, and Keras.

ABOUT US

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

ABOUT THE TEAM

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

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