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Internship Deep Reinforcement Learning Jobs in Washington

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

You will work on problems involving reinforcement learning, model evaluations, language models ... We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new ...

AIToolboard

AI Research Scientist

Washington, DC · On-site

$120 - $180/hr

... deep learning, natural language processing, computer vision, or reinforcement learning. Design ... Mentor junior researchers and interns. Participate in defining the future research roadmap for the ...

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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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The most popular types of Deep Reinforcement Learning jobs in Washington are:

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For Internship Deep Reinforcement Learning jobs in Washington, the most frequently searched job titles are:

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What cities in Washington are hiring for Internship Deep Reinforcement Learning jobs?

Cities in Washington with the most Internship Deep Reinforcement Learning job openings:

Internship Deep Reinforcement Learning jobs near you
Infographic showing various Internship Deep Reinforcement Learning job openings in Washington as of June 2026, with employment types broken down into 18% As Needed, 27% Full Time, 9% Part Time, 18% Temporary, and 28% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

Machine Learning Engineer

Laurel, MD • On-site

Johns Hopkins Applied Physics Laboratory
Manufacturing • 5 - 10K employees

9.6

Company rating: 9.6 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

2nd of 74 rated research

Great coworkers

People enjoy working here

Good employer

Learn more about Johns Hopkins Applied Physics Laboratoryexternal

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 25 days ago

Job description

Description
Do you have demonstrated machine learning experience and want to apply that experience to solving a wide variety of complex problems in this rapidly evolving field?
Do you thrive in a collaborative research environment, working alongside an energetic, multidisciplinary team of scientists and engineers?
Are you ready to help the US secure and maintain leadership in the development and deployment of AI/ML algorithms for non-kinetic defense systems?
If so, we're looking for someone like you to join our team at APL!
We are seeking an experienced Machine Learning Engineer who will contribute to all phases of the machine learning algorithm development and implementation. You will be joining a team of engineers and scientists who are at the forefront of APL's mission to provide innovative solutions to critical challenges.
As a Machine Learning Engineer, you will...
  • Design, implement, and evaluate advanced machine learning algorithms to solve challenging real-world planning, perception, coordination, and control problems in support of national defense.
  • Develop software pipelines to integrate data streams, simulation environments, and intelligent decision-making algorithms.
  • Work with technologies and concepts at the cutting edge of AI, including but not limited to: deep reinforcement learning, foundation models, large language models, convolutional/recurrent/graph neural networks, computer vision, and physics-based modeling and simulation tools.
  • Collaborate closely with the talented team of scientists and engineers in our group and with others across APL.
  • Engage directly with sponsors to communicate proposed concepts, solutions, and analysis.

Qualifications
You meet the minimum requirements for the job if you...
  • Have a Bachelor's degree in Mathematics, Physics, Engineering, Computer Science, or a related field.
  • Have at least 2+ years of experience in machine learning and data science fields.
  • Have at least one year of hands-on experience applying/developing machine learning algorithms using common libraries such as PyTorch or TensorFlow.
  • Have strong foundational knowledge in at least two of the following: classification, clustering, deep learning, reinforcement learning, computer vision (object detection and visual tracking), multi-agent systems, or optimization/control theory.
  • Have demonstrated experience in working with version control software like Git.
  • Have strong, effective communication skills both verbal and written.
  • Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

You 'll go above and beyond our minimum requirements if you...
  • Have an MS in Mathematics, Physics, Engineering, Computer Science, or a related field.
  • Have 5+ years of experience in designing and implementing AI/ML algorithms for a variety of datasets.
  • Have proven experience applying state-of-the-art deep learning techniques to solve distributed resource allocation problems.
  • Have hands-on experience building computer vision pipelines for detection, tracking, segmentation, or multi-modal sensor fusion.
  • Have experience with modeling and simulation platforms such as AFSIM, Blender, Unity, or Unreal.
  • Are comfortable working in high performance computing environments (GPU/CPU clusters).
  • Have proficiency in one or more of the following technology areas: multi-agent reinforcement learning, geometric deep learning, multi-modal sensor fusion, agentic AI.
  • Have a track record of writing deployable, production-level code (Python, C/C++) for real-world applications.

#LI-KW1
About Us
Why Work at APL?
The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.
At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at https://www.jhuapl.edu/careers.
All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accessibility@jhuapl.edu.
The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.
Minimum Rate
$100,000 Annually
Maximum Rate
$245,000 Annually

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