1

Robotic Imitation Learning Jobs (NOW HIRING)

Research Scientist

San Francisco, CA · On-site

$120K - $250K/yr

Research and implement state-of-the-art robot learning policies, including reinforcement learning and imitation learning-based techniques * Build reliable, high-speed robot autonomy software stack ...

Familiarity with reinforcement learning, imitation learning, or adaptive control techniques applicable to robotics. * Experience deploying ML models in real‑time or embedded environments. * Nice to ...

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Staff AI Research Engineer

Salem, OR · On-site +1

$216K - $338K/yr

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Design and train RL and imitation learning policies for locomotion, manipulation, or whole-body ... Instrument robot deployments and analyze failure modes to feed improvements back into training

Showing results 41-60

Robotic Imitation Learning information

See salary details

$15

$28

$45

How much do robotic imitation learning jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for robotic imitation learning in the United States is $28.24, according to ZipRecruiter salary data. Most workers in this role earn between $22.12 and $32.93 per hour, depending on experience, location, and employer.

What is robotic imitation learning?

Robotic imitation learning is a technique in robotics where robots learn to perform tasks by observing and mimicking human actions or demonstrations. Instead of being explicitly programmed, the robot uses data from demonstrations to infer the necessary actions or behaviors. This approach can help robots learn complex tasks more efficiently and adapt to new situations by leveraging human expertise. It is widely used in areas such as manufacturing, service robotics, and autonomous vehicles.

What are some typical challenges faced when working on robotic imitation learning projects, and how can they be addressed?

Professionals in robotic imitation learning often encounter challenges such as collecting high-quality demonstration data, ensuring generalization across varied tasks, and managing the sim-to-real gap when transferring learned behaviors from simulation to physical robots. Addressing these challenges typically involves developing robust data collection pipelines, leveraging domain adaptation techniques, and collaborating closely with hardware engineers to fine-tune models in real-world environments. Teamwork with researchers specializing in computer vision, reinforcement learning, and robotics hardware is crucial for overcoming these hurdles and achieving reliable robot performance.

What are the key skills and qualifications needed to thrive in robotic imitation learning, and why are they important?

To thrive in Robotic Imitation Learning, you need a strong background in robotics, machine learning, and computer science, often supported by an advanced degree in a related field. Familiarity with programming languages like Python or C++, machine learning frameworks (e.g., TensorFlow, PyTorch), and robotics simulation tools such as ROS or Gazebo is essential. Strong problem-solving skills, creativity, and effective communication are crucial soft skills for developing innovative solutions and collaborating with multidisciplinary teams. These skills and qualities are important to advance the field, ensure robust algorithm development, and enable efficient deployment of robotic systems in real-world environments.

What is the difference between Robotic Imitation Learning vs Robotic Software Engineer?

AspectRobotic Imitation LearningRobotic Software Engineer
Required CredentialsAdvanced degrees in AI, Robotics, or Computer ScienceBachelor's or Master's in Software Engineering, Computer Science, or related fields
Work EnvironmentResearch labs, AI development teams, robotics startupsIndustrial, research, or manufacturing settings involving robotics software development
Industry UsageDeveloping algorithms for robots to learn behaviors through imitationDesigning, coding, and maintaining robotic control software

Robotic Imitation Learning focuses on creating algorithms that enable robots to learn behaviors by mimicking demonstrations, often requiring expertise in AI and machine learning. In contrast, a Robotic Software Engineer develops and maintains the software systems that control robots, emphasizing programming skills and software development. Both roles are essential in robotics but serve different functions within the industry.

What other helpful pages are available for Robotic Imitation Learning?

Other pages related to Robotic Imitation Learning:

Infographic showing various Robotic Imitation Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $58,744 per year, or $28.2 per hour.

Research Engineer / Research Scientist, Robotics and Physical AI

San Francisco, CA • On-site

Clera
1 - 10 employees

$100K - $300K/yr

Other

Posted 9 days ago


Job description

About the Role

This is a hands-on research and engineering role at a small, early-stage AI company focused on generating high-quality, verifier-grounded training data for robotics and frontier AI models. You will work across the full stack, from learning algorithms and simulation to real hardware, helping push robot capabilities from synthetic environments into reliable real-world deployment.

What You'll Do
  • Develop learning systems for robots, including policies, world models, and vision-language-action models.

  • Build and maintain simulation environments and evaluation frameworks for robotic tasks.

  • Apply and advance computer vision and perception algorithms for autonomous robots.

  • Build pipelines connecting simulation and real-world robot deployment, covering both real-to-sim and sim-to-real workflows.

  • Work with robot hardware, sensors, actuators, and embedded systems to prototype and test capabilities.

  • Train and evaluate reinforcement-learning and imitation-learning systems for manipulation, locomotion, or mobile platforms.

  • Integrate robot platforms to validate algorithms in the physical world.

  • Create tools and infrastructure that accelerate robotics research and development.

  • Collaborate with external robotics companies and research labs on technical problems.

What We're Looking For
  • Open to all experience levels, including new graduates and seasoned researchers or engineers.

  • Strong software skills in C++ and Python.

  • Practical experience with deep learning, reinforcement learning, and computer vision.

  • Experience developing and maintaining robotics simulation environments and evaluation frameworks (e.g., Isaac Lab, MuJoCo, MJX, Genesis).

  • Experience training and evaluating RL and imitation-learning systems for manipulation, locomotion, or mobile platforms.

  • Hands-on experience with robot hardware, sensors, actuators, CAD or Blender, and embedded systems.

  • Familiarity with modern ML tools such as LLMs, Transformers, multimodal AI, diffusion models, or AI agents is a plus.

  • Comfortable moving fluidly between research, engineering, experimentation, and product development in a small team.

  • US work authorization required; visa sponsorship is not available.

Compensation & Benefits

Salary range: $100,000 to $300,000 USD annually, depending on experience. Equity participation is expected at this stage. Visa sponsorship is not available.

Location

On-site in San Francisco, California, United States. This role is not eligible for remote work.

#J-18808-Ljbffr