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Reinforcement Learning Robotics Jobs in Washington, DC

Sierra Nevada Corporation

Sr AI/ML Engineer

Herndon, VA

$107K - $147K/yr

Hands-on experience with reinforcement learning and real-time systems applicable to MPC ... Background in autonomous systems, robotics, or sensor fusion. * Familiarity with Agile/DevOps ...

AstraZeneca

Agentic AI Scientist

Gaithersburg, MD · On-site

Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable ... Be part of a digitally-enabled environment that impacts all parts of the business-from robotic ...

AstraZeneca

Agentic AI Scientist

Gaithersburg, MD

Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable ... Be part of a digitally-enabled environment that impacts all parts of the business-from robotic ...

AstraZeneca

Agentic AI Scientist

Gaithersburg, MD · On-site

Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable ... Be part of a digitally-enabled environment that impacts all parts of the business-from robotic ...

Booz Allen Hamilton

Autonomy SME, Lead

Washington, DC

$112K - $257K/yr

Develop reinforcement learning and autonomy algorithms for navigation and mission execution ... Experience with robotics middleware, such as ROS or ROS2 * Experience with computer vision or ...

Booz Allen Hamilton, Inc.

Autonomy SME, Lead

Washington, DC · On-site +1

$116K - $152K/yr

Develop reinforcement learning and autonomy algorithms for navigation and mission execution ... Experience with robotics middleware, such as ROS or ROS2 * Experience with computer vision or ...

Trusted Space, LLC

Autonomy Engineer

Chantilly, VA · Hybrid

$140K - $190K/yr

Experience implementing, applying, and analyzing behavior of reinforcement learning algorithms ... These domains can be robotics, drones, etc * Experience working with large code bases with multiple ...

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Reinforcement Learning Robotics information

What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?

One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.

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

To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.

What is reinforcement learning in robotics?

Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.

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

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData analysis, predictive modeling, AI applications

Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.

What are popular job titles related to Reinforcement Learning Robotics jobs in Washington, DC? For Reinforcement Learning Robotics jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Robotics jobs in Washington, DC look for? The top searched job categories for Reinforcement Learning Robotics jobs in Washington, DC are:
Reinforcement Learning Robotics jobs near you

Machine Learning Engineer

Cymertek Corporation

Annapolis Junction, MD • On-site

Full-time

Posted 16 days ago

Job description

Job Summary:
Cymertek Corporation is seeking a talented and innovative Machine Learning Engineer to join their team. The role involves designing, developing, and deploying machine learning models to solve complex problems and improve decision-making processes.
Responsibilities:
• Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning)
• Ability to design, implement, and optimize machine learning models and workflows
• Experience working with large, complex datasets
• Knowledge of data preprocessing and feature engineering
• Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure)
• Strong problem-solving skills and analytical thinking
Qualifications:
Required:
• Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning)
• Ability to design, implement, and optimize machine learning models and workflows
• Experience working with large, complex datasets
• Knowledge of data preprocessing and feature engineering
• Familiarity with cloud-based platforms for machine learning (e.g., AWS, Google Cloud, Azure)
• Strong problem-solving skills and analytical thinking
• Proficiency in programming languages (e.g., Python, R, Java)
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Expertise in model evaluation techniques and metrics
• Strong knowledge of version control tools (e.g., Git)
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau)
• Understanding of database technologies (e.g., SQL, NoSQL)
• Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Software Engineering, Electrical Engineering, Robotics, Computational Biology, Physics, etc.
Preferred:
• Experience with natural language processing (NLP)
• Knowledge of deep learning techniques (e.g., CNNs, RNNs)
• Familiarity with deployment tools (e.g., Docker, Kubernetes)
• Experience with data augmentation and synthetic data generation
• Ability to collaborate in cross-functional teams (e.g., engineers, product managers)
• Knowledge of edge computing and model optimization for deployment
Company:
With headquarters in Maryland, Cymertek [/'sī-mer-tek/] Corporation provides superior consulting services for the implementation of high quality information systems. Founded in 2010, the company is headquartered in Laurel, USA, with a team of 11-50 employees. The company is currently Early Stage.

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