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Reinforcement Learning Engineer Jobs in Reston, VA

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly ... reinforcement learning, and deep learning. * Experience with data processing tools like Pandas ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Supervised, unsupervised, and reinforcement learning * Neural networks, decision trees, ensemble ... Feature engineering and preprocessing * Data augmentation strategies for training robustness

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... Proficiency in programming languages (e.g., Python, R, Java) * Experience with machine learning ...

Senior Machine Learning Engineer

Mclean, VA

$105K - $145K/yr

Supervised, unsupervised, and reinforcement learning * Neural networks, decision trees, ensemble ... Feature engineering and preprocessing * Data augmentation strategies for training robustness

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Supervised, unsupervised, and reinforcement learning * Neural networks, decision trees, ensemble ... Feature engineering and preprocessing * Data augmentation strategies for training robustness

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Familiarity or experience with model distillation, synthetic data generation, reinforcement ...

Machine Learning Engineer We're seeking a skilled Machine Learning Engineer to build and deploy ... Experience with reinforcement learning algorithms and applications * Digital signal processing ...

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Reinforcement Learning Engineer information

See Reston, VA salary details

$39.5K

$120.5K

$199.2K

How much do reinforcement learning engineer jobs pay per year?

As of Jun 23, 2026, the average yearly pay for reinforcement learning engineer in Reston, VA is $120,540.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,300.00 and $157,600.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Reston, VA? For Reinforcement Learning Engineer jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Engineer jobs in Reston, VA look for? The top searched job categories for Reinforcement Learning Engineer jobs in Reston, VA are:
What cities near Reston, VA are hiring for Reinforcement Learning Engineer jobs? Cities near Reston, VA with the most Reinforcement Learning Engineer job openings:

Machine Learning Engineer

Full Scope

Reston, VA

Other

Posted 7 days ago


Job description

Job Title:Machine Learning Engineer
Location:Fort Meade, MD
Required Clearance: TS/SCI w/ Full-Scope Poly
Salary:Competitive
We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, data science, and software engineering. You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value.
Key Responsibilities:
  • Design, develop, and implement machine learning models and algorithms to solve real-world problems.
  • Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
  • Conduct data analysis and preprocessing to ensure high-quality data for model training.
  • Optimize and fine-tune models for performance, accuracy, and scalability.
  • Deploy machine learning models into production and monitor their performance.
  • Develop and maintain machine learning pipelines and infrastructure.
  • Stay current with the latest research and advancements in machine learning and AI.
  • Participate in code reviews, team meetings, and contribute to a collaborative development environment.
  • Document processes, models, and findings comprehensively.
Qualifications:
  • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
  • Proven experience as a Machine Learning Engineer or in a similar role.
  • Strong proficiency in programming languages such as Python, R, or Java.
  • Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
  • Solid understanding of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning.
  • Experience with data processing tools like Pandas, NumPy, and data visualization tools such as Matplotlib or Seaborn.
  • Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model deployment and scaling.
  • Strong problem-solving skills and the ability to think critically and analytically.
  • Excellent communication and teamwork skills.
Preferred Qualifications:
  • Experience with natural language processing (NLP) and computer vision.
  • Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
  • Knowledge of software development best practices and version control systems like Git.
  • Experience with containerization tools like Docker and orchestration tools like Kubernetes.
  • Previous experience in a fast-paced, startup environment.