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Internship Deep Reinforcement Learning Jobs in Virginia

General Atomics

Data Scientist I

Charlottesville, VA · On-site

Trained deep reinforcement learning algorithms for coordination of multiple, autonomous goals. * Performed nonlinear optimization for resource allocation with dynamic uncertainty and/or non ...

General Atomics

Data Scientist I

Charlottesville, VA · On-site

$68K - $116K/yr

Trained deep reinforcement learning algorithms for coordination of multiple, autonomous goals. * Performed nonlinear optimization for resource allocation with dynamic uncertainty and/or non ...

EchoStar

Senior AI/ML Architect

Herndon, VA · On-site

$177K - $240K/yr

Familiarity with advanced AI techniques--deep reinforcement learning, federated learning, and model explainability preferred * Knowledge of AI ethics, regulatory compliance in telecom, and data ...

Staffingine LLC

AI Platform Developer

Herndon, VA · On-site

Machine Learning (ML) & Deep Learning (DL): You'll need a deep understanding of ML concepts (supervised, unsupervised, reinforcement learning) and neural network architectures like CNNs and RNNs.

NAVA Software Solutions

Data Scientist

Merrifield, VA · On-site

... reinforcement learning * Advanced knowledge of advanced techniques such as: dimension reduction ... Demonstrates a deep understanding of the modeling lifecycle * Advanced skill data mining, data ...

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Internship Deep Reinforcement Learning information

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 Virginia as of July 2026, with employment types broken down into 70% Full Time, 20% Part Time, and 10% Temporary. Highlights an 92% In-person, and 8% Remote job distribution.
Data Scientist I

Data Scientist I

General Atomics

Charlottesville, VA • On-site

Other

Posted 2 days ago

General Atomics rating

9.0

Company rating: 9.0 out of 10

Based on 39 frontline employees who took The Breakroom Quiz

10th of 71 rated aerospace companies

Job description

Job Summary
General Atomics Integrated Intelligence, Inc. (GA-Intelligence), an affiliate of General Atomics, was founded in 1989 and is located in Charlottesville, VA. It provides custom software development and innovative information engineering solutions to customers in government and private industry. We build and develop best-in-class all domain and globally focused situational awareness capabilities that process petabytes of data from numerous streaming data sources in near real time. Our systems apply state-of-the-art algorithms and machine learning techniques to extract features and fuse data from multiple phenomenologies to form a rich live view of objects in the sky, on the sea, and on the ground. These analytics are designed to determine not just where something is, but what it is, where it's been and what it's doing. All of this "data to knowledge" is made available to end users in our own browser-based application for visualization, analysis, and understanding. We always want to do more, and that's where you come in!
DUTIES AND RESPONSIBILITIES:
  • Design, develop and program methods that integrate to systems that consolidate and analyze unstructured, diverse data sources to generate actionable insights and solutions for client services and product enhancement.
  • Develop, refine, deploy, and support statistical and machine learning models utilizing state of the art approaches.
  • Communicate insights and findings from analysis and experiments to product, service, and business managers.
  • Identify opportunities for product and customer process improvement using statistical and machine learning models to improve the effectiveness of different courses of action
  • Create and utilize moderately complex algorithms and approaches, clean and synthesize training/test data, create/run simulations, and perform analysis of alternatives to best meet stakeholder requirements.
  • Develop and deploy data visualization in order to communicate moderately complex concepts and data in a simple, actionable manner.
  • Interact with product and service teams to identify questions and issues for data analysis and experiments.
  • Document findings for business stakeholders as required. May create proposals, cost estimates, and whitepapers suitable for publication.
  • Represent the organization as a contact with internal and external representatives.
  • Maintain the strict confidentiality of sensitive information.
  • Responsible for observing all laws, regulations and other applicable obligations wherever and whenever business is conducted on behalf of the Company. Expected to work in a safe manner in accordance with established operating procedures and practices.
We recognize and appreciate the value and contributions of individuals with diverse backgrounds and experiences and welcome all qualified individuals to apply.
Job Qualifications
  • Typically requires a bachelor's or master's degree in data science, applied mathematics, statistics, computer science, or related technical/quantitative discipline from an accredited institution. May substitute equivalent experience in lieu of education.
  • Must be able to work both independently and in a team environment.
  • Able to work extended hours and travel to support project needs as required.
  • Ability to obtain and maintain DoD security clearance is required.
PREFERRED SKILLS AND EXPERIENCE:
  • Prior experience with gym, Unity, Unreal, or some other simulation environments.
  • Researched or applied autonomy in a highly-latent, low-bandwidth, or otherwise resource constrained environment.
  • Trained deep reinforcement learning algorithms for coordination of multiple, autonomous goals.
  • Performed nonlinear optimization for resource allocation with dynamic uncertainty and/or non-observable variables.
  • Prior experience in domains/industries like: robotics, space mission planning, large industrial facility automation, robust and resilient communications.
  • Familiarity with common machine learning libraries for implementation (sklearn, weka, tensorflow, torch, etc)
  • Proficiency in one or more of: Python, C++, Java, R, Julia, Matlab
  • Ability to understand and implement new models from the literature
  • Experience with geospatial data and analytics.
  • Familiarity with Intelligence Community and DoD mission sets.

What General Atomics employees say

Pay

Benefits

Hours and flexibility

Workplace

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About General Atomics

Sourced by ZipRecruiter

General Atomics (GA), and its affiliated companies, is one of the world's leading resources for high-technology systems development ranging from the nuclear fuel cycle to remotely piloted aircraft, airborne sensors, and advanced electric, electronic, wireless and laser technologies.

Industry

Space research administration

Company size

10,000+ Employees

Headquarters location

San Diego, CA, US

Year founded

1955

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