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Internship Deep Reinforcement Learning Jobs in Virginia

Cymertek Corporation

Machine Learning Engineer

Reston, VA · On-site

... unsupervised, reinforcement learning) • Ability to design, implement, and optimize machine ... deep learning techniques (e.g., CNNs, RNNs) • Familiarity with deployment tools (e.g., Docker ...

Cymertek Corporation

Machine Learning Engineer

Reston, VA · On-site

... unsupervised, reinforcement learning) • Ability to design, implement, and optimize machine ... deep learning techniques (e.g., CNNs, RNNs) • Familiarity with deployment tools (e.g., Docker ...

Cymertek Corporation

Machine Learning Engineer

Reston, VA · On-site

... unsupervised, reinforcement learning) • Ability to design, implement, and optimize machine ... deep learning techniques (e.g., CNNs, RNNs) • Familiarity with deployment tools (e.g., Docker ...

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Internship Deep Reinforcement Learning information

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.

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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 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

AI Research Scientist - Machine Learning

Richmond, VA • On-site

AIToolboard
See more jobs at AIToolboardexternal

Other

Posted 23 days ago

Job description

Jobs / AI Research Scientist - Machine Learning

AI Research Scientist - Machine Learning

Full-time

About the Role

Our client is seeking a brilliant and innovative AI Research Scientist specializing in Machine Learning to join their cutting-edge R&D team in Richmond, Virginia, US. This role is at the forefront of developing next-generation AI technologies and algorithms. You will be responsible for designing, implementing, and evaluating advanced machine learning models, conducting groundbreaking research, andcontributing to high-impact AI applications. The ideal candidate possesses a strong academic background, a deep understanding of ML principles, and a passion for pushing the boundaries of artificial intelligence.Key Responsibilities:Conduct advanced research in machine learning, deep learning, and related AI fields. Design, develop, and implement novel algorithms and models for complex AI problems. Experiment with various ML techniques, including supervised, unsupervised, reinforcement learning, and neural networks. Analyze large datasets, preprocess data, and extract meaningful features for model training. Evaluate model performance, identify areas for improvement, and iterate on designs. Collaborate with software engineers to deploy and integrate AI models into production systems. Stay current with the latest advancements in AI and ML research through literature review and conference participation. Publish research findings in leading scientific journals and present at conferences. Mentor junior researchers and interns, fostering a collaborative research environment. Contribute to the intellectual property portfolio through patent applications.Qualifications:Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field. Proven research experience demonstrated through publications in top-tier AI/ML conferences and journals (e.g., NeurIPS, ICML, ICLR, CVPR). Strong theoretical foundation in machine learning, deep learning, and statistical modeling. Proficiency in programming languages such as Python, and experience with ML libraries like TensorFlow, PyTorch, scikit-learn. Experience with data manipulation and analysis tools. Ability to design and conduct rigorous experiments, interpret results, and draw insightful conclusions. Excellent problem-solving skills and creativity in developing novel solutions. Strong communication and presentation skills, with the ability to articulate complex technical concepts. Experience with distributed computing frameworks (e.g., Spark) is a plus. Experience in specific domains like NLP, computer vision, or reinforcement learning is highly desirable. Join a forward-thinking team that is shaping the future of AI. This exciting opportunity is based in Richmond, Virginia, US .

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