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Reinforcement Learning Robotics Jobs in California

Horizon Robotics

Research Scientist (Cupertino)

Cupertino, CA · On-site

$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 ...

Posted today

Clera

Robot Learning Engineer

San Francisco, CA · On-site

Experience with ROS 2 * Experience with computer vision and sensor data processing for robotics applications * Practical knowledge of reinforcement learning and/or imitation learning for robot ...

Clera

Robot Learning Engineer

San Francisco, CA · On-site

Experience with ROS 2 * Experience with computer vision and sensor data processing for robotics applications * Practical knowledge of reinforcement learning and/or imitation learning for robot ...

Horizon Robotics

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 ...

Horizon Robotics

Research Scientist

Cupertino, CA · On-site

$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 ...

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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 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.

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

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 looking for exceptional Research Engineers / Scientists to design learning systems that allow agents to plan over long horizons, learn effective strategies, and improve through experience.
This role sits at the intersection of reinforcement learning, large language models, and real-world autonomous systems. Autonomous systems must operate reliably in complex, dynamic environments. We believe the next generation of autonomy will involve learning agents that continuously improve through interaction, feedback, and large-scale data. You will help build the learning systems that power these agents.
 
Key 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.
Basic Qualifications
  • 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 Qualifications
  • 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.
 
What do we provide:
  • A fun, supportive and engaging environment.
  • Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving.
  • Opportunity to work on cutting edge technologies with the top talent in the field.
  • Competitive compensation package.
  • Snacks, lunches and fun activities.
 
The base salary range for this full-time position is $244,140 - $413,160, 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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