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Reinforcement Learning Robotics Jobs in California

Research Scientist

Cupertino, CA · Hybrid

$150K - $300K/yr

We are looking for someone with expertise in and enthusiasm for machine learning research, especially in Robotics, Embodied AI, Reinforcement learning (RL) , etc. As a Research Scientist in the team ...

... for robotics applications • Deep expertise in modern robot learning techniques (reinforcement learning, imitation learning, behavior cloning, etc.) • Strong proficiency in Python and deep ...

Join to apply for the Research Engineer, Reinforcement Learning role at 1X Join to apply for the ... This enables our robots to be safe and robust in a variety of conditions. Responsibilities Full ...

New

... reinforcement learning, or related learning-based manipulation methods • Proficiency in Python and/or C++ for robotics and ML systems • Experience with modern deep learning frameworks (e.g ...

Join to apply for the Research Engineer, Reinforcement Learning role at 1X Join to apply for the ... This enables our robots to be safe and robust in a variety of conditions. Responsibilities Full ...

New

... reinforcement learning, or related learning-based manipulation methods • Proficiency in Python and/or C++ for robotics and ML systems • Experience with modern deep learning frameworks (e.g ...

Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications. * Design and conduct experiments to train RL models and conduct real-world tests. * Collaborate ...

... reinforcement learning, or related learning-based manipulation methods • Proficiency in Python and/or C++ for robotics and ML systems • Experience with modern deep learning frameworks (e.g ...

Showing results 41-60

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 California? For Reinforcement Learning Robotics jobs in California, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Robotics jobs in California look for? The top searched job categories for Reinforcement Learning Robotics jobs in California are:
What cities in California are hiring for Reinforcement Learning Robotics jobs? Cities in California with the most Reinforcement Learning Robotics job openings:
Infographic showing various Reinforcement Learning Robotics job openings in California as of August 2026, with employment types broken down into 95% Full Time, and 5% Part Time. Highlights an 85% In-person, 5% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer, Reinforcement Learning (San Francisco)

Skild AI

San Francisco, CA • On-site

$300K/yr

Full-time

Re-posted 10 days ago


Job description

Get AI-powered advice on this job and more exclusive features.

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.

Company Overview

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.

Position Overview

We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.


Responsibilities

  • Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.

  • Design and conduct experiments to train RL models and conduct real-world tests.

  • Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.

  • Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.

  • Analyze and interpret experimental results, iterating on model design to achieve desired performance.

  • Stay up-to-date with the latest research and advancements in reinforcement learning.


Preferred Qualifications

  • BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.

  • Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.

  • Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).

  • Strong background in algorithms, data structures, and software engineering principles.

  • Experience with physics simulation engines and tools for training RL.

  • Deep understanding of state-of-the-art machine learning techniques and models.

  • Extensive industry experience with reinforcement learning and robotic systems.

Base Salary Range: $100,000 USD - $300,000 USD

Seniority level
  • Seniority levelEntry level
Employment type
  • Employment typeFull-time
Job function
  • Job functionEngineering and Information Technology
  • IndustriesSoftware Development

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