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Internship Deep Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... This engineer will leverage their deep expertise in RL to solve critical locomotion and ...

MLabs

Reinforcement Learning Engineer

New York, NY

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

poolside

Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

What unites us is our deep care for what we build together. We're in a race that requires hard work ... ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ...

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

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

As of Jul 12, 2026, the average hourly pay for internship deep reinforcement learning in the United States is $17.04, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

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 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 are the key skills and qualifications needed to thrive as an Intern in Deep Reinforcement Learning, and why are they important?

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.

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Internship Deep Reinforcement Learning jobs near you
Infographic showing various Internship Deep Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $35,436 per year, or $17 per hour.
Data Scientist, Reinforcement Learning

Data Scientist, Reinforcement Learning

ExxonMobil

Spring, TX • On-site

Part-time

Medical, Dental, Vision, Life, Retirement

Re-posted 13 days ago

ExxonMobil rating

6.0

Company rating: 6.0 out of 10

Based on 225 frontline employees who took The Breakroom Quiz

57th of 75 rated oil and gas companies

Job description

Your role on our team
Pioneer the application of reinforcement learning (RL) and sequential decision-making to high-impact challenges across ExxonMobil's upstream, downstream, and commercial operations.
Collaborate with engineers, scientists, and business stakeholders to turn complex operational and planning problems into deployable, production-grade RL solutions.
Advance the organization's capabilities in reinforcement learning, decision optimization, and autonomous control as part of the Modeling, Optimization, and Data Science (MODS) team.
What you will do
  • Design, develop, and deploy reinforcement learning solutions for real-world energy applications such as production optimization, process control, supply chain scheduling, drilling optimization, and resource allocation.
  • Formulate sequential decision problems by defining state spaces, action spaces, reward structures, transition dynamics, and operational constraints with domain experts.
  • Develop RL agents using model-free methods (e.g., PPO, SAC, TD3, DQN where appropriate) and model-based approaches, selecting methods based on problem requirements, safety, and data availability.
  • Build and use simulation environments and digital twins for offline training, policy evaluation, and validation before real-world deployment.
  • Apply safe and constrained RL techniques to ensure agents operate within operational and safety limits.
  • Integrate RL solutions with existing optimization, simulation, and control systems across real-time and planning use cases.
  • Partner with data scientists and ML engineers to operationalize solutions, including training pipelines, monitoring, retraining, and performance tracking.
  • Benchmark RL against traditional methods such as LP, MIP, heuristic search, MPC, and stochastic optimization to identify best-fit approaches.
  • Stay current with advances in offline RL, safe RL, multi-agent RL, hierarchical RL, and model-based RL.
  • Share knowledge, publish findings where appropriate, and mentor peers on RL best practices.

About you
Desired Skills:
  • Experienced AI/ML professional with strong expertise in reinforcement learning, sequential decision-making, optimization, and real-world deployment.
  • 5+ years of experience in AI/ML, optimization, or related fields, including at least 2 years in reinforcement learning, sequential decision-making, or optimal control.
  • Master's or PhD in Computer Science, Machine Learning, Operations Research, Control Theory, Robotics, Applied Mathematics, Engineering, or a related quantitative field.
  • Deep understanding of RL fundamentals, including MDPs, dynamic programming, temporal-difference learning, policy gradients, and actor-critic methods.
  • Proven experience building RL systems end-to-end, from environment and reward design through training, evaluation, and deployment.
  • Experience with simulation environments, digital twins, or system models.
  • Strong background in statistics, probability, optimization, control theory, and algorithm design.
  • Proficiency in Python, PyTorch and/or TensorFlow, plus RL tools such as Stable Baselines3, RLlib, and Gymnasium.
  • Strong communication and collaboration skills, including the ability to explain technical concepts to non-technical stakeholders.

Preferred Skills:
  • Experience applying RL or decision optimization in industrial domains such as process control, robotics, autonomous systems, supply chain, energy systems, or operations research.
  • Familiarity with offline (batch) RL, safe RL, and multi-agent RL.
  • Knowledge of model-based RL, MPC, and hybrid RL-control approaches.
  • Understanding of classical optimization methods and how RL complements them.
  • Experience with physics-informed or hybrid mechanistic/ML modeling and domain-informed reward or constraint design.
  • Familiarity with platforms such as Azure ML, Azure OpenAI, Databricks, and MLOps tools such as MLflow or Weights & Biases.
  • Experience in the energy industry or other asset-intensive, safety-critical sectors.

Your benefits
An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.
We offer you:
  • Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life.
  • Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match.
  • Workplace Flexibility: We have several programs such as "Flex your Day", providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
  • Comprehensive medical, dental, and vision plans.
  • Culture of Health: Programs and resources to support your wellbeing.
  • Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you.
  • Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.

More information on our Company's benefits can be found at www.exxonmobilfamily.com.
Please note benefits may be changed from time to time without notice, subject to applicable law.
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