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Robotics Manipulation Reinforcement Learning Jobs

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

... and manipulation tasks with physical hardware. * Drive the entire development cycle, from ... Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co ...

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 ... and manipulation challenges and deliver breakthrough results on physical hardware. The primary ...

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Robotics Manipulation Reinforcement Learning information

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How much do robotics manipulation reinforcement learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for robotics manipulation reinforcement learning in the United States is $96,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $102,000.00 per year, depending on experience, location, and employer.

What is robotics manipulation reinforcement learning?

Robotics Manipulation Reinforcement Learning is a field of artificial intelligence where robots learn to interact with and manipulate objects in their environment through trial and error, using feedback to improve their performance. This involves developing algorithms that enable robots to autonomously acquire complex motor skills, such as grasping, pushing, or assembling objects, without explicit human programming for every task. Reinforcement learning provides the framework for robots to optimize their actions by receiving rewards or penalties based on their success in manipulating objects, making them more adaptable to new and unstructured environments.

What are some common challenges faced when applying reinforcement learning in robotics manipulation tasks?

A common challenge in robotics manipulation with reinforcement learning (RL) is dealing with the complexity and unpredictability of real-world environments. Unlike simulations, physical robots must handle noisy sensors, actuator delays, and unexpected interactions with objects. Training RL models can also be time-consuming and data-intensive, requiring robust simulation environments or safe real-world data collection strategies. Collaboration with hardware engineers and domain experts is often essential to troubleshoot issues and optimize learning efficiency. Successful practitioners are adaptable and proactive in bridging the gap between theory and real-world robotic performance.

What are the key skills and qualifications needed to thrive as a robotics manipulation reinforcement learning engineer, and why are they important?

To thrive as a Robotics Manipulation Reinforcement Learning Engineer, you need a solid background in robotics, machine learning (especially reinforcement learning), computer science, and typically an advanced degree such as a Master's or PhD. Experience with programming languages like Python or C++, frameworks such as TensorFlow or PyTorch, and robotics middleware like ROS are commonly required, along with familiarity with simulation environments like Gazebo or Mujoco. Strong problem-solving, collaboration, and communication skills set top performers apart as they integrate complex algorithms into real-world robotic systems. These skills and qualities are crucial for developing effective, adaptive robotic solutions that perform sophisticated manipulation tasks in dynamic environments.

What is the difference between Robotics Manipulation Reinforcement Learning vs Robotics Software Engineer?

AspectRobotics Manipulation Reinforcement LearningRobotics Software Engineer
Required CredentialsAdvanced degrees in AI, Robotics, or related fields; knowledge of reinforcement learningBachelor's or master's in Computer Science, Robotics, or Software Engineering
Work EnvironmentResearch labs, AI startups, academia focusing on machine learning applications in roboticsIndustrial settings, manufacturing, or tech companies developing robotic systems
Industry UsageDeveloping algorithms for robotic manipulation tasks using reinforcement learningBuilding, testing, and deploying robotic software systems

Robotics Manipulation Reinforcement Learning specialists focus on creating algorithms that enable robots to learn manipulation tasks through reinforcement learning techniques. In contrast, Robotics Software Engineers develop and maintain the software systems that control robotic hardware. While both roles require programming skills, the former emphasizes machine learning and AI research, whereas the latter concentrates on software development and integration in robotic applications.

What other helpful pages are available for Robotics Manipulation Reinforcement Learning?

Other pages related to Robotics Manipulation Reinforcement Learning:

Infographic showing various Robotics Manipulation Reinforcement Learning job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Hybrid job distribution, with an average salary of $96,000 per year, or $46.2 per hour.

Senior Reinforcement Learning Engineer

Sunnyvale, CA • On-site

Apptronik
Industrial Automation Equipment Manufacturing • 11 - 50 employees
Learn more about Apptronikexternal

$230K - $260K/yr

Full-time

Posted 27 days ago

Key responsibilities

  • Implement and deploy state-of-the-art reinforcement learning algorithms to achieve performance on locomotion and manipulation tasks with physical hardware.

  • Drive the development cycle from simulation prototyping to transferring and fine-tuning policies on robots.

  • Mentor junior engineers by providing technical guidance, conducting code reviews, and sharing best practices.

Job description

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 expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

  • Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
  • Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
  • Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
  • Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
  • Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
  • Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
  • Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.

SKILLS AND REQUIREMENTS

  • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
  • Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
  • Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
  • A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
  • A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
  • A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

EDUCATION and/or EXPERIENCE

  • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
  • A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
  • Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
  • A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

PHYSICAL REQUIREMENTS 

  • Prolonged periods of sitting at a desk and working on a computer
  • Must be able to lift 15 pounds at times
  • Vision to read printed materials and a computer screen
  • Hearing and speech to communicate

Compensation: The annual compensation for this position is $230,000 - $260,000 (USD) 

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