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Reinforcement Learning Robotics Jobs (NOW HIRING)

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 ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting ... JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ...

Team Red Dog

AI Robotics Engineer

Redmond, WA · On-site

Robotics Simulation & Machine Learning - Experience working with simulation environments or robotic hardware, including reinforcement learning, diffusion models, or robotic AI workflows. Opportunity ...

Team Red Dog

AI Robotics Engineer

Redmond, WA

Robotics Simulation & Machine Learning - Experience working with simulation environments or robotic hardware, including reinforcement learning, diffusion models, or robotic AI workflows. Opportunity ...

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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 July 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution.

Staff Robotics Engineer / Tech Lead - Whole-Body Control & Robot Learning

XPENG

Santa Clara, CA • On-site

Full-time

Posted 8 days ago

Job description

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.
We are now expanding into the development of general-purpose humanoid robots aimed at automating repetitive tasks and assisting people in their daily lives.
We are seeking a highly motivated Staff Robotics Engineer / Tech Lead to join our US robotics team. This role is ideal for a strong hands-on engineer who can contribute deeply to humanoid robot control, whole-body motion generation, robot learning, and sim-to-real deployment, while helping coordinate technical direction and mentor a small team. The ideal candidate brings strong engineering judgment, clear communication, and the ability to help other engineers move faster and make better technical decisions.
Key Responsibilities:
  • Develop control and learning-based algorithms forhumanoid robot locomotion, whole-body control, motion generation, and sim-to-real transfer.
  • Contribute to the technical direction of the robotics team by identifying key problems, proposing practical solutions, and helping prioritize engineering efforts.
  • Work with simulation, software, hardware, AI, data, and China-based engineering teams to translate robot performance goals into executable plans.
  • Mentor other engineers through design reviews, code reviews, experiments, and technical discussions.
Minimum Requirements:
  • Master's or Ph.D. degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field.
  • 5+ years of relevant experience inreinforcement learning, robotics, robot learning, control systems, or related areas.
  • Strong background in robot dynamics, control, motion planning, reinforcement learning, imitation learning, or whole-body control.
  • Familiarity with modern robot learning methods such as PPO, SAC, behavior cloning, diffusion policies, imitation learning, etc.
  • Excellent communication skills, with the ability to work across disciplines, locations, and organizational boundaries.
  • Strong ownership mindset and ability to operate effectively in a fast-moving, ambiguous, research-to-product environment.
Preferred Requirements:
  • Publications or strong project experience in robotics, reinforcement learning, legged locomotion, humanoid control, or embodied AI are a plus.
  • Experience technically leading projects or mentoring other engineers.
  • Hands-on experience with legged robot control, testing, or operation.

What do we provide:
  • A supportive, engaging environment with opportunities to make a significant impact on the future of robotics.
  • Opportunities to work on cutting-edge technologies with top talent in the field.
  • Competitive compensation, equity, benefits.
  • Lunches, snacks, and team activities.

The base salary range for this full-time position is $215,280-$364,320, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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