ZipRecruiter

Log In

1

Reinforcement Learning Intern Jobs in Texas (NOW HIRING)

Apara Autism Center

Regional Clinical Director

Dallas, TX · Hybrid

$89K - $121K/yr

Support the implementation of clinic-wide reinforcement systems and methods for tracking staff ... Manage intern supervision and training across all regions RBT Training & Professional Development:

All JobsReinforcement Learning Intern JobsReinforcement Learning Intern Jobs in Texas

Reinforcement Learning Intern information

See Texas salary details

$8

$15

$22

How much do reinforcement learning intern jobs pay per hour?

As of Jul 27, 2026, the average hourly pay for reinforcement learning intern in Texas is $15.87, according to ZipRecruiter salary data. Most workers in this role earn between $13.41 and $17.93 per hour, depending on experience, location, and employer.

What kinds of projects or tasks can I expect to work on as a Reinforcement Learning Intern?

As a Reinforcement Learning Intern, you will typically work on tasks such as designing, implementing, and testing reinforcement learning algorithms, analyzing experimental results, and assisting with data preprocessing or environment development. You may also collaborate with senior researchers and engineers, participate in code reviews, and contribute to technical discussions or team meetings. In many organizations, interns are given the chance to work on real-world problems—ranging from optimizing robotic control systems to enhancing recommendation engines. This hands-on experience not only builds your technical expertise but also helps you develop valuable teamwork and communication skills, preparing you for a future career in AI or machine learning.

Does IQVIA pay interns?

Reinforcement Learning Interns at IQVIA are typically paid positions, with compensation varying based on location, experience, and program duration. Internships often include stipends or hourly wages and may require a commitment of several months, providing practical experience in data analysis and machine learning tools.

Is ML a high paying job?

Reinforcement Learning Intern positions are typically considered entry-level roles in the machine learning field, and salaries can vary based on location, company, and experience. Generally, machine learning roles tend to offer higher-than-average starting salaries compared to many other tech positions, especially for those with specialized skills in algorithms, programming, and data analysis.

What are the big 4 internships?

The 'Big 4' internships typically refer to summer internship programs at the four largest professional services firms: Deloitte, PricewaterhouseCoopers (PwC), Ernst & Young (EY), and KPMG. These internships are highly competitive and often focus on areas such as consulting, audit, tax, and advisory services, providing valuable experience for students pursuing careers in accounting and finance.

What is a Reinforcement Learning Intern job?

A Reinforcement Learning (RL) Intern is responsible for researching, developing, and testing RL algorithms to solve complex problems. They typically work on tasks such as implementing reinforcement learning models, optimizing reward functions, and running experiments in simulated environments. Interns collaborate with researchers and engineers to refine models and improve the efficiency of RL systems. They usually have experience in machine learning, deep learning, and programming languages like Python. The role provides hands-on experience in applying RL techniques to real-world applications.

How much do ML interns get paid?

Reinforcement Learning interns, as a type of machine learning intern, typically earn between $20 and $40 per hour, depending on the company, location, and level of experience. Paid internships often include opportunities to work with tools like Python and TensorFlow and may be full-time or part-time during the summer or academic year.

What are the key skills and qualifications needed to thrive in the Reinforcement Learning Intern position, and why are they important?

To thrive as a Reinforcement Learning Intern, you need strong knowledge of machine learning fundamentals, programming proficiency (usually in Python), and a background in mathematics or computer science, often demonstrated through academic coursework or relevant projects. Familiarity with popular machine learning libraries such as TensorFlow, PyTorch, and RL-specific frameworks like OpenAI Gym is typically expected. Effective problem-solving skills, attention to detail, and the ability to communicate technical findings clearly are valuable soft skills in this position. These capabilities enable interns to contribute meaningfully to research and development efforts, bridging theory and practical application in real-world reinforcement learning projects.

What are the most commonly searched types of Reinforcement Learning jobs in Texas? The most popular types of Reinforcement Learning jobs in Texas are:
What are popular job titles related to Reinforcement Learning Intern jobs in Texas? For Reinforcement Learning Intern jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Intern jobs in Texas look for? The top searched job categories for Reinforcement Learning Intern jobs in Texas are:
What cities in Texas are hiring for Reinforcement Learning Intern jobs? Cities in Texas with the most Reinforcement Learning Intern job openings:
Reinforcement Learning Intern jobs near you
Infographic showing various Reinforcement Learning Intern job openings in Texas as of July 2026, with employment types broken down into 100% Full Time. Highlights an 75% In-person, and 25% Hybrid job distribution, with an average salary of $33,014 per year, or $15.9 per hour.
Data Science Machine Learning Internship (Summer 2027)

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International LLC

Houston, TX

Full-time

Posted 6 days ago

Job description

Application Deadline: September 1, 11:59pm EST

Program Summary - Commercial Technology Internships

Company Overview:

Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.

Position Overview:

CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. Our Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.

Responsibilities:

  • Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions.
  • Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches.
  • Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams.
  • Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis.
  • Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights.
  • Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.

Qualifications:

  • Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics.
  • Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
  • Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds.
  • Strong analytical skills with demonstrated attention to detail.

Apply