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Internship Deep Reinforcement Learning Jobs in Dallas, TX

Waabi

Research Scientist, Learnable Planner

Dallas, TX · On-site +1

$158K - $269K/yr

You will... - Design and execute on a research agenda for deep-learning based motion planning for ... reinforcement learning, planning and search, perception and prediction, simulation, foundation ...

Waabi

Research Scientist, Learnable Planner

Dallas, TX · On-site +1

$158K - $269K/yr

You will... - Design and execute on a research agenda for deep-learning based motion planning for ... reinforcement learning, planning and search, perception and prediction, simulation, foundation ...

Waabi

Research Scientist, Learnable Planner

Dallas, TX · On-site +1

$158K - $269K/yr

You will... - Design and execute on a research agenda for deep-learning based motion planning for ... reinforcement learning, planning and search, perception and prediction, simulation, foundation ...

IT America Inc

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

... reinforcement learning, generative models, foundation models, planning and search, and other ... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

... reinforcement learning, generative models, foundation models, planning and search, and other ... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

... reinforcement learning, generative models, foundation models, planning and search, and other ... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX

$101K - $138K/yr

Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning. Experience with cloud platforms such as ...

BI Solutions Inc

WatsonX Assistant

Coppell, TX · On-site

... Deep Learning - Understanding AI models, neural networks, and reinforcement learning 2. Natural Language Processing (NLP) - Developing AI systems that interpret and generate human language 3. Multi ...

CCS INC

Gen AI Engineer

Plano, TX · On-site

$40 - $50/hr

Deep understanding of LLMs, embeddings, vector databases (e.g., FAISS, Pinecone, Weaviate ... Implement prompt engineering, instruction tuning, and reinforcement learning from human feedback ...

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How much do internship deep reinforcement learning jobs pay per hour?

As of Aug 29, 2026, the average hourly pay for internship deep reinforcement learning in Dallas, TX is $16.85, according to ZipRecruiter salary data. Most workers in this role earn between $14.28 and $19.04 per hour, depending on experience, location, and employer.

What is an internship in deep reinforcement learning?

An internship in Deep Reinforcement Learning (DRL) is a temporary, hands-on position where interns learn and apply state-of-the-art machine learning algorithms that enable computers to learn decision-making tasks through trial and error. Interns typically work on projects involving neural networks, reward systems, and environments like games or simulations. These internships provide valuable experience with frameworks such as TensorFlow or PyTorch, and exposure to current research in artificial intelligence. The experience helps students or recent graduates build technical skills and prepare for careers in AI research or industry.

What types of projects or tasks can I expect to work on during a deep reinforcement learning internship?

As a Deep Reinforcement Learning (DRL) intern, you'll typically work on projects involving the development, implementation, and evaluation of reinforcement learning algorithms. This might include tasks like training agents in simulated environments, tuning hyperparameters, analyzing performance metrics, and collaborating with team members to integrate DRL solutions into larger systems. You'll also likely spend time reading recent research papers, experimenting with frameworks such as TensorFlow or PyTorch, and presenting your findings to the research team. Collaboration with mentors and other interns is common, and you'll gain hands-on experience that prepares you for more advanced roles in AI research or engineering.

What are the key skills and qualifications needed to thrive as an intern in deep reinforcement learning?

To thrive as an Intern in Deep Reinforcement Learning, you need a solid background in mathematics (especially linear algebra, probability, and calculus), programming (Python), and foundational knowledge in machine learning principles, usually supported by ongoing or completed coursework in computer science or related fields. Familiarity with frameworks and tools such as TensorFlow, PyTorch, OpenAI Gym, and experience using version control systems like Git are typically required. Analytical thinking, curiosity, and effective communication are essential soft skills for collaborating on research problems and sharing complex findings. These skills and qualities are crucial for contributing to innovative projects and successfully navigating the challenges of cutting-edge AI research.

What is the difference between Internship Deep Reinforcement Learning vs Data Science Intern?

AspectInternship Deep Reinforcement LearningData Science Intern
Required SkillsMachine learning, programming (Python), reinforcement learning conceptsStatistics, data analysis, programming (Python/R), data visualization
Work EnvironmentResearch labs, AI companies, tech startupsBusiness analytics, tech firms, consulting agencies
Industry UsageAI research, robotics, autonomous systemsBusiness intelligence, marketing, finance

Internship Deep Reinforcement Learning focuses on developing algorithms that enable systems to learn through trial and error, often in AI research or robotics. Data Science Internships involve analyzing data to extract insights and support decision-making. While both roles require programming skills, reinforcement learning emphasizes AI-specific techniques, whereas data science centers on statistical analysis and data visualization.

What are the most commonly searched types of Deep Reinforcement Learning jobs in Dallas, TX?

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Internship Deep Reinforcement Learning jobs near you

Executive Director - Applied Artificial Intelligence Machine Learning

Plano, TX • On-site

JPMorgan Chase & Co.
Finance and Insurance • 10K+ employees

8.0

Company rating: 8.0 out of 10

Based on 497 frontline employees who took The Breakroom Quiz

72nd of 172 rated banks

People enjoy working here

Good employer

Recommended by students

Learn more about JPMorgan Chase & Co.external

Full-time

Medical, Retirement

Re-posted 21 days ago

Job description


As an Applied AI/ML Executive Director within our dynamic team, you will apply your quantitative, data science, and analytical skills to complex problems. As a Machine Learning Director, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. You will collaborate with various teams and actively participate in our knowledge sharing community. We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you.
Job responsibilities
  • Develop advanced agentic AI solutions involving structured and unstructed data, casual analytics, machine learning, deep learning, reinforcement learning, and optimization.
  • Design robust agent architectures combining LLM reasoning with tools, structured data, and APIs spanning state, memory, and context management, plus loop engineering (plan/act/observe, verification, termination, and fallback/escalation).
  • Engineer reliable agent-driven workflows emphasizing correctness, traceability, and control-aware behavior (guardrails, approvals, auditable decision paths).
  • Build knowledge-centric reasoning layers, including knowledge graphs and hybrid retrieval (RAG + graph + structured sources) to improve grounding and accuracy.
  • Drive specification-driven development: author specs and contracts (schemas, validators, tool/skill interfaces) and build evaluation/regression harnesses.
  • Advance agent quality via recursive self-improvement through automated evaluation and critique loops, red-team feedback, skill/prompt instruction optimization, and outcome-driven dataset curation (human-in-the-loop as needed).
  • Coach and mentor AI/ML team members, setting a high bar for engineering rigor and research depth.

Required qualifications, capabilities, and skills
  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or with at least 5 years of industry experience or an MS with at least 7 years of industry or research experience in the field.
  • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
  • Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
  • Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
  • Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred qualifications, capabilities , and skills:
  • Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
  • Knowledge in search/ranking, Reinforcement Learning or Meta Learning
  • Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal

About Us
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
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.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
About the Team
J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.

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