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Reinforcement Learning Intern Jobs in Texas (NOW HIRING)

Rockwell Automation

Intern, Applied AI

Austin, TX

$22 - $40/hr

Prior intern or co-op with Rockwell Automation * Pursuing a degree in Mechanical Engineering ... Experience in the following Python, machine learning or Generative AI, reinforcement learning ...

New

Rockwellautomation

Intern, Applied AI

Austin, TX · On-site

$22 - $40/hr

Prior intern or co-op with Rockwell Automation * Pursuing a degree in Mechanical Engineering ... Experience in the following Python, machine learning or Generative AI, reinforcement learning ...

New

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 Aug 2, 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.

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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.

Software Engineer Intern - ML Systems

Apptronik

Austin, TX

Other

Posted 4 days ago

Job description

JOB SUMMARY
Apptronik is seeking a Software Engineering Intern to join our ML Ops team for a 12-week fall project. In this role, you will work at the intersection of robotics and applied machine learning - building data annotation tooling and optimizing ML models that run on humanoid hardware. You
will help close the loop between raw robot experience data and deployable, hardware-ready models for Apollo, Apptronik's humanoid robot.

You will take ownership of two interconnected workstreams: (1) building or extending data annotation tools that let the team efficiently label and curate robot experience data, and (2) applying ML model optimization techniques - quantization, distillation, and inference profiling - to improve the throughput and efficiency of models deployed on physical systems. You will work alongside the simulation engineering, data platform, and learning teams, and contribute directly to how Apptronik turns ML research into production robot behavior.

ESSENTIAL DUTIES AND RESPONSIBILITIES

  • Data Annotation Tooling: Design and implement tooling for efficient annotation and curation of robot experience data - including sensor observations, trajectories, and task outcomes - in formats compatible with the team's data lake (MCAP, S3/MinIO).
  • ML Model Optimization: Profile, quantize, and/or distill ML models (RL policies or action heads) to reduce inference latency and memory footprint for deployment on robot hardware.
  • Hardware-Aware Evaluation: Build evaluation harnesses that benchmark optimized model performance against baseline, tracking metrics relevant to physical deployment (latency, memory, task success rate).
  • Integration with Existing Infra: Connect annotation outputs and optimized model artifacts with the team's existing artifact storage (S3/MinIO), training pipelines, and Kubernetes-based execution environment.
  • Documentation & Handoff: Produce design docs, runbooks, and example configurations so tooling can be adopted by controls, learning, and data platform teams after the internship.

SKILLS AND REQUIREMENTS

  • Python Proficiency: Demonstrated ability to write clean, tested, maintainable code for ML tooling, data pipelines, and automation.
  • Linux & Development Tools: Comfortable in a Linux environment; competence with Git, Docker, and modern Python tooling (pytest, uv/poetry, type hints).
  • ML Framework Experience: Hands-on experience with PyTorch or similar; familiarity with model quantization (INT8/FP16), ONNX export, or TensorRT is a plus.
  • Robotics Background: Coursework or project experience with robotic systems - kinematics, control, sensors, or simulation (ROS, MuJoCo, Isaac Sim, Gazebo, or comparable).
  • Data Pipeline Exposure: Experience moving data between annotation, training, evaluation, and storage stages.
  • Annotation Tooling (preferred): Prior experience with data annotation workflows, labeling interfaces (Label Studio, CVAT, custom tooling), or human-in-the-loop data pipelines.
  • Reinforcement Learning o: Familiarity with RL training loops, policy rollouts models.

EDUCATION and/or EXPERIENCE

  • Current enrollment in a Bachelor's or Master's degree program in Computer Science, Electrical Engineering, Robotics, or a related field.
  • Experience with projects involving robotics, ML model deployment, data annotation, or
    developer tooling is ideal.

PHYSICAL REQUIREMENTS

  • Prolonged periods of sitting at a desk and working on a computer.
  • Must be able to lift 15 pounds at times.
  • Vision to read printed materials and a computer screen.
  • Hearing and speech to communicate.

Apply