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Robotics Manipulation Reinforcement Learning Jobs

Dexmate

Robot learning engineer

Santa Clara, CA · On-site

... robot manipulation, navigation, and control--from simulation to deployment on physical systems • Develop novel approaches to enhance robot dexterity and mobility using reinforcement learning ...

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Helix AI Engineer, Robot Learning

San Jose, CA · On-site

Experience with behavior cloning, reinforcement learning , or related learning-based manipulation methods * Proficiency in Python and/or C++ for robotics and ML systems * Experience with modern deep ...

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Helix AI Engineer, Robot Learning

San Jose, CA · On-site

Experience with behavior cloning, reinforcement learning , or related learning-based manipulation methods * Proficiency in Python and/or C++ for robotics and ML systems * Experience with modern deep ...

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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 Jun 23, 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 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 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 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 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.
Infographic showing various Robotics Manipulation Reinforcement Learning job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $96,000 per year, or $46.2 per hour.

Machine Learning Engineer - Robot Manipulation

Maven Robotics

San Francisco, CA

Full-time

Posted 21 days ago

Job description

Company Overview
Maven Robotics is building the world's leading general-purpose AI robots.
We are currently operating in stealth and are growing the world's best team in AI robotics. We are looking for self-starters that are the world's best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.
Role Description
We are looking to recruit an exceptional Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy robot manipulation algorithms that enable assembly and material movement tasks.
In this role you will:
  • Design and implement machine learning algorithms, with a focus on reinforcement learning (RL) and imitation learning (IL), to enable robotic manipulators to perform complex tasks in dynamic environments.
  • Translate high-level objectives into machine learning problems and deploy robust, scalable models to real-world robotic systems.
  • Integrate your ML solutions into existing robotics workflows, ensuring that models are performant in both simulated and real-world settings.
  • Drive innovation by incorporating the latest research in machine learning into practical applications that push the boundaries of robotic manipulation.
  • Take ownership of critical ML projects, seeing them through from conception to successful deployment.
  • Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.
Qualifications
Must-have:
  • MS or PhD in machine learning, computer science, robotics, or a related field.
  • Strong practical experience in training and deploying machine learning models for real-world applications.
  • Deep understanding of reinforcement learning (RL) and imitation learning (IL) and their application to robotics.
  • Proficiency in programming languages and tools commonly used in machine learning (e.g., Python, PyTorch).
  • Experience with data collection, preprocessing, and management in the context of training ML models.
  • Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
  • Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:
  • Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
  • Experience in:
    • Designing and implementing reward functions for complex manipulation tasks.
    • Developing models that can handle noisy, incomplete, or sparse data.
    • Deployment of ML models to edge devices for real-time inference.
    • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
    • Using reinforcement learning frameworks, e.g. Stable Baselines, RLlib, or similar.
  • General knowledge of robotics principles, including kinematics, dynamics, and control.
  • Publications or contributions to the machine learning community, particularly in areas related to robotics or reinforcement learning.

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