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Reinforcement Learning Robotics Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting ... As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and ...

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Auzmor

Robotics & AI Research Engineer

Des Moines, IA · On-site

Robotics & AI Research Engineer Description Auzmor is redefining workforce training by seamlessly ... You will develop state-of-the-art machine learning models, reinforcement learning algorithms, and ...

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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.

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Reinforcement Learning Robotics jobs near you
Infographic showing various Reinforcement Learning Robotics job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Staff Research Engineer - Reinforcement Learning for AI Agents

XPENG

Santa Clara, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 21 days ago

Job description

Job Summary:
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles. They are seeking exceptional Research Engineers / Scientists to design learning systems for agents that can plan over long horizons and improve through experience.
Responsibilities:
• Reinforcement learning methods for LLM-driven agents and decision systems.
• Policy optimization for long-horizon reasoning and planning.
• Learning from human or AI feedback (RLHF / RLAIF).
• Agent training pipelines built on top of our agent infrastructure platform.
• Evaluation and benchmarking systems for agent capabilities.
• Learning loops that integrate real-world and simulation data.
• Contribute to AI systems that continuously improve after deployment.
Qualifications:
Required:
• MS or PhD in Computer Science, AI, Machine Learning, Robotics, or a related field.
• Strong background in reinforcement learning or machine learning.
• Experience implementing RL algorithms such as PPO, Actor-Critic, or policy gradient methods.
• Strong programming skills in Python with PyTorch or JAX.
• Experience building ML training systems or infrastructure.
Preferred:
• Experience with RLHF or preference learning.
• Experience with LLM agents or tool-using AI systems.
• Multi-agent systems or long-horizon planning.
• Simulation environments for RL.
• Publications in NeurIPS, ICML, ICLR, ACL, or related venues.
Company:
XPENG is a leading Chinese Smart EV company that designs, develops, manufactures, and markets Smart EVs that appeal to the large and growing base of technology-savvy middle-class consumers. Founded in 2014, the company is headquartered in Guangzhou, CHN, with a team of 10001+ employees. The company is currently Late Stage.

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